Relative Feedback Increases Disparities in Effort and Performance in Crowdsourcing Contests
Open accessAbstract
Rankings and leaderboards are often used in crowdsourcing contests and online communities to motivate individual contributions but feedback based on social comparison can also have negative effects. Here, we study the unequal effects of such feedback on individual effort and performance for individuals of different ability. We hypothesize that the effects of social comparison differ for top performers and bottom performers in a way that the inequality between the two increases. We use a quasi-experimental design to test our predictions with data from Topcoder, a large online crowdsourcing platform that publishes computer programming contests. We find that in contests where the submitted code is evaluated against others' submissions, rather than using an absolute scale, top performers increase their effort while bottom performers decrease it. As a result, relative scoring leads to better outcomes for those at the top but lower engagement for bottom performers. Our findings expose an important but overlooked drawback from using gamified competitions, rankings, and relative evaluations, with potential implications for crowdsourcing markets, online learning environments, online communities, and organizations in general.
AI snapshot
Written from the paper’s full text.
TL;DR
In a quasi-experiment on Topcoder, relative (social-comparison) scoring increased effort and performance for high-rated coders but decreased effort for low-rated coders, widening disparities. The paper supports hypotheses on effort and performance dispersion but finds no significant evidence that relative scoring increases predictability of performance.
Key findings
- H1 supported: In relatively scored vs. absolutely scored challenges, the bottom 15% by rating wrote/edited 46–245 fewer lines, while the top 15% wrote/edited 300–383 more; the regression difference was significant, ∆χ²(2df)=7.633, p=0.022. Bottom 45% made 1.6–2.5 fewer submissions, ∆χ²(2df)=7.537, p=0.023; duration difference was not significant, ∆χ²(2df)=4.682, p=0.096.
- H2 supported: Code complexity disparity was greater under relative scoring, ∆χ²(3df)=11.532, p=0.009. The effect was mainly driven by coders above the 50th percentile, who submitted about 30–74 additional independent logical paths; lowest-rated coders did not develop solutions as much.
- H3 not supported: Predictability of performance from prior performance did not significantly differ, ∆χ²(2df)=3.974, p=0.137. First-submission timing, length, and complexity did not differ significantly, suggesting final outcomes were not driven by a priori expectations or dispositions.
Methods
- Data: Topcoder marathon matches, covering 189,659 successful code submissions by 17,000 individuals in 367 challenges from 15 December 2005 to 1 April 2018; scoring type was manually classified by coders as absolute vs. relative.
- Design: Coarsened exact matching on challenge size, duration, difficulty, reward structure, and task yielded 52 challenges in 16 strata. A crossover quasi-experiment restricted to coders with both scoring types in the same stratum and a rating left 1,239 observations of 319 coders over 42 challenges in 12 strata, with 12,914 successful submissions.
- Analysis: Mixed-effects models with random intercepts for strata, challenges, and individuals; gamma regressions for code lines, submissions, and cyclomatic complexity; beta regressions for time in challenge and proximity to rating; stratum weights and controls for previous challenges; estimand was the local average treatment effect for the treated.
Limitations
- Stated: Matching and the quasi-experimental crossover design discard most data and narrow the claims; findings may not transfer to contests with monetary rewards; matching cannot entirely preclude confounding, e.g., relative scoring may be applied to more open-ended, less structured challenges; no survey or random assignment was available.
- Stated: Cyclomatic complexity is limited as a performance measure; it correlates strongly with lines of code (Pearson 0.878), so results are interpreted narrowly as elaboration/development, not maintainability, optimization, or legibility.
- Stated/evident: The matched sample does not represent the Topcoder population; results concern experienced coders and do not extend to first-time contestants; matched challenges had no monetary or nonmonetary prizes (Table 4), and subjects were more experienced and higher rated than average contestants; Topcoder is not representative, replication is needed, and upward vs. downward comparison or smaller collaboration-centered groups may differ.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
16 of 77 cited sources checked · 2 supported · 1 flagged · accuracy not yet measured
- Premisecitation
Empirical research on contests and tournaments contradicts Festinger's expectation that underachievers increase effort.
“However, empirical research on contests and tournaments contradicts Festinger's expectation that underachievers increase effort.”
Quote in the paperSubstantial claim3 of 3 sources checked- cites Cason et al., 2010 source
The Results (section 3.2; page unavailable) show that lower-ability subjects were less likely to enter winner-take-all contests when facing strong opponents. The same section reports that individual performance did not depend on competitors’ abilities. These findings do not show underachievers increasing effort, but the paper tests contest entry and performance, not Festinger’s specific expectation about underachievers’ effort. (LLM, conf=0.85)
Experimental — accuracy not yet measured - cites Dechenaux et al., 2015 source
The review does not present a general contradiction of the expectation. In §6.2, p. 30, it reports that participants who ranked worse than expected increased their performance and that contestants who lagged behind tended to increase effort. It also describes mixed findings on feedback, including quitting in some tournaments. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measured - cites van Dijk, Sonnemans, & van Winden, 2001 source
- Premisecitation
Compared to piece-rate schemes, contests, all-pay auctions, and tournaments result in higher variance in individual effort.
“Compared to piece-rate schemes, contests, all-pay auctions, and tournaments result in higher variance in individual effort (Cason et al., 2010;Dechenaux et al., 2015;van Dijk, Sonnemans, & van Winden, 2001).”
Quote in the paperSubstantial claim3 of 3 sources checked- cites Cason et al., 2010 source
The Introduction (page unavailable) says winner-take-all incentives may produce greater variance in players’ effort than piece-rate wages. Section 2.2 (page unavailable) identifies the paper’s winner-take-all contest as an all-pay auction. The citing sentence overstates a qualified possibility as a general result for contests and tournaments, including designs beyond winner-take-all. (LLM, conf=0.88)
Experimental — accuracy not yet measured - cites Dechenaux et al., 2015 source
Section 2.3 (p. 10) describes high variance in individual effort in rank-order tournaments. But it also reports that effort variance was comparable between tournaments and piece-rate schemes when their payoff functions were made similar. The cited paper therefore does not support the unqualified claim that tournaments—and contests and all-pay auctions generally—produce higher effort variance than piece rates. (LLM, conf=0.87)
Experimental — accuracy not yet measured - cites van Dijk, Sonnemans, & van Winden, 2001 source
The Hypothesis 4 results (p. 23) report significantly greater variability in tournaments than in the piece-rate condition. The cited paper does not establish the same comparison for contests generally or all-pay auctions, which the citing sentence also attributes to its citations. (LLM, conf=0.88)
Experimental — accuracy not yet measured
- Interpretationcitation
Those at the top of the chart may experience the opposite effect, whereby a provisional reward for a serendipitous move could increase their confidence and motivation to persevere.
“Conversely, those at the top of the chart may experience the opposite effect, whereby a provisional reward for a serendipitous move could increase their confidence and motivation to persevere (Restivo & van de Rijt, 2012).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Restivo & van de Rijt, 2012 source
The Methods describe randomly awarding barnstars to a sample of the top 1% of Wikipedia editors, and Results and Discussion report greater sustained productivity and less discontinuation among recipients (page numbers not provided). This supports the possibility that a reward helps high-performing contributors persevere. The paper does not measure confidence or study rewards for a serendipitous move. (LLM, conf=0.85)
Experimental — accuracy not yet measured
- Findingown data§ Relative Feedback Increases Disparities in Effort and Performance in Crowdsourcing Contests: Evidence from a Quasi-Experiment on Topcoder
Relative feedback increases disparities in effort and performance in crowdsourcing contests.
“Relative Feedback Increases Disparities in Effort and Performance in Crowdsourcing Contests: Evidence from a Quasi-Experiment on Topcoder”
Quote in the paperSubstantial claim - Findingown data
The difference in number of submissions between relative and absolute scoring by rating stratum is statistically significant (∆χ2(2df)=7.537, p=0.023).
“The difference for number of submissions is statistically significant (∆χ 2 (2df ) = 7.537, p = 0.023 between Model 2.1 and 2.2 in Table 1)”
Quote in the paperSubstantial claim
- Findingown data
The difference in performance between relative and absolute scoring is statistically significant (∆χ2(3df)=11.532, p=0.009).
“The statistical models reveal that this difference is significant (∆χ 2 (3df ) = 11.532,p = 0.009 between Models 4.1 and 4.2 in Table 2)”
Quote in the paperSubstantial claim - Findingown data
The difference in performance, measured by computational complexity of the final submitted code, between low-rated and high-rated individuals is more extreme in relatively scored challenges than in absolutely scored challenges.
“We find that the difference in the level of performance, as measured by the computational complexity of the final submitted code, is more extreme between low-rated and high-rated individuals in challenges with relative scoring compared to challenges with absolute scoring (Fig. 2).”
Quote in the paperSubstantial claim - Findingown data
The difference in lines written between relative and absolute scoring by rating stratum is statistically significant in regression models controlling for stratum, challenge, individual, ordering effects, and weighting by stratum (∆χ2(2df)=7.633, p=0.022).
“This difference is statistically significant when we test it in regression models that account for variability by stratum, challenge, and individual, control for ordering effects, and weigh the observations by stratum (∆χ 2 (2df ) = 7.633, p = 0.022 between Model 1.1 and Model 1.2 in Table 1).”
Quote in the paperSubstantial claim - Findingown data
Higher code complexity from coders above the 50th percentile largely accounts for the performance effect.
“The effect is mainly driven by the higher code complexity produced by coders rated above the 50th percentile.”
Quote in the paperSubstantial claim - Findingown data
In relatively scored challenges, contestants in the top 15% by rating write and edit more lines of code than in absolutely scored challenges, by 300-383 lines on average.
“while a contestant in the top 15% writes and edits 300-383 more”
Quote in the paperSubstantial claim - Findingown data
Those rated in the bottom 45% submitted 1.6-2.5 fewer entries on average in relatively scored challenges than in absolutely scored challenges.
“contestants in the bottom 45% by rating make on average 1.6-2.5 fewer submissions in relatively scored challenges compared to absolutely scored challenges”
Quote in the paperSubstantial claim - Findingown data
In relatively scored challenges, contestants in the bottom 15% by rating write and edit fewer lines of code than in absolutely scored challenges, by 46-245 lines on average.
“on average, a contestant in the bottom 15% by rating writes and edits 46-245 fewer lines in relatively scored challenges compared to absolutely scored challenges”
Quote in the paperSubstantial claim - Findingown data
The difference in duration of participation between relative and absolute scoring by rating stratum is not statistically significant (∆χ2(2df)=4.682, p=0.096).
“although not the one for duration (∆χ 2 (2df ) = 4.682, p = 0.096 between Model 3.1 and 3.2 in Table 1)”
Quote in the paperNot a substantial claim - Findingown data
None of the matched challenges have monetary or nonmonetary prizes.
“We note that none of the matched challenges have monetary or nonmonetary prizes (Table 4).”
Quote in the paperSubstantial claim - Findingown data
The difference in predictability between relative and absolute scoring does not reach statistical significance (∆χ2(2df)=3.974, p=0.137).
“This difference, however, does not reach statistical significance in our data and models: comparing the model without treatment effects (Model 5.1 in Table 2) and the model with treatment effects for the intercept and slope of the relationship between rating and predictability in the treated group (Model 5.2 in Table 2) yields ∆χ 2 (2df ) = 3.974 and p = 0.137.”
Quote in the paperSubstantial claim - Findingown data
Coders rated in the top half produced solutions with roughly 30-74 more independent logical paths under relative scoring.
“those in the top half by rating submit software with about 30-74 additional independent logical paths when scoring is relative.”
Quote in the paperSubstantial claim - Findingown data
At challenge start, contestants in absolute and relative scoring conditions did not differ significantly.
“The differences between contestants in the challenges with absolute and relative scoring are not statistically significant at the start of the challenges”
Quote in the paperSubstantial claim - Findingown data
In relatively scored challenges, coders submitted fewer entries than in absolutely scored challenges (coefficient −0.292, p<0.01).
“Relative scoring | | -0.269 * | -0.292 * * | | -0.341 | |”
Quote in the paperSubstantial claim - Findingown data
Newcomers in relative-scoring contests leave sooner than those in absolute-scoring contests.
“the only statistically significant difference is that first-timers in relatively scored challenges tend to exit the contest faster than first-timers in absolutely scored challenges (Table 7).”
Quote in the paperNot a substantial claim - Findingown data
The reduction in the number of submissions under relative scoring is smaller for coders with higher ratings (interaction coefficient 0.338, p<0.05).
“Relative scoring × Rating | | 0.405 * * | | 0.338 * | | 0.531 * |”
Quote in the paperSubstantial claim - Findingown data
Higher-rated coders produce more complex code in relatively scored challenges than in absolutely scored ones (interaction coefficient 1.480, p<0.01).
“Relative scoring × Rating | | 1.480 * * | | -0.349 * |”
Quote in the paperSubstantial claim - Findingown data
Under relative scoring, higher-rated coders' final-score percentile is less close to their rating percentile (interaction coefficient −0.349, p<0.05).
“Relative scoring × Rating | | 1.480 * * | | -0.349 * |”
Quote in the paperSubstantial claim - Findingown data
For the analyzed final submissions, cyclomatic complexity and line count correlate at 0.878.
“Indeed, the Pearson correlation between the two for the final code submissions we analyze is 0.878.”
Quote in the paperSubstantial claim - Findingown data
The timing, length, and complexity of the first submission do not differ significantly between relatively scored and absolutely scored challenges.
“There are no significant differences in the timing (top left), length (top right), and complexity (bottom) of the first submission between relatively scored and absolutely scored challenges.”
Quote in the paperNot a substantial claim - Premisecitation
People tend to seek better standing relative to others, pursuing distinction and status.
“Even more importantly, we tend to seek a better standing in relation to others, that is, we pursue distinction and status (Fiske, 2011;Payne, 2017).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Fiske, 2011
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Payne, 2017
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationown data
Using gamified competitions, rankings, and relative evaluations has an important but overlooked drawback.
“Our findings expose an important but overlooked drawback from using gamified competitions, rankings, and relative evaluations, with potential implications for crowdsourcing markets, online learning environments, online communities, and organizations in general.”
Quote in the paperSubstantial claim - Premisecitation
Competition increases individual effort.
“Thus, social comparison induces competition (Festinger, 1954;Garcia, Tor, & Schiff, 2013), and competition increases individual effort.”
Quote in the paperSubstantial claim0 of 2 sources checked - Premisecitation
Social comparison induces competition.
“Thus, social comparison induces competition (Festinger, 1954;Garcia, Tor, & Schiff, 2013), and competition increases individual effort.”
Quote in the paperSubstantial claim0 of 2 sources checked - Premisecitation
Contributions to a public good increase in dispersion when individuals are incentivized with tournament rewards for contributing.
“Similarly, the contributions to a public good increase in dispersion when individuals are incentivized with tournament rewards for contributing (Dickinson & Isaac, 1998).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Dickinson & Isaac, 1998
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
In the case of arbitrary or unfair pre-existing differences, more predictable outcomes signal lack of equality of opportunity and upward mobility.
“However, in the case of arbitrary or unfair pre-existing differences, more predictable outcomes signal the lack of equality of opportunity and upward mobility (Van de Werfhorst & Mijs, 2010).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Van de Werfhorst & Mijs, 2010 source
- Premisecitation
In the case of an inherent quality or virtue, more predictable outcomes imply more equality and fairness.
“In the case of an inherent quality or virtue, more predictable outcomes imply more equality and fairness (Cook & Hegtvedt, 1983;Jasso, 1983;Salganik et al., 2006).”
Quote in the paperSubstantial claim0 of 3 sources checked - Premisecitation
Within-group competition for scarce resources diminishes cooperation.
“Within-group competition for scarce resources reduces trust and trustworthiness (Charness, Masclet, & Villeval, 2013;Chowdhury & Gürtler, 2015;Liu, Lin, & Xin, 2014), decreases contributions to public goods (Barker & Barclay, 2016), diminishes cooperation (West et al., 2006), and hinders agreement in bargaining situations (Barclay & Stoller, 2014).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites West et al., 2006 source
- Interpretationown data
The results support H2, the hypothesis that relative scoring increases the disparity in performance between low-rated and high-rated individuals.
“providing support for H2.”
Quote in the paperSubstantial claim - Study design and conductown data
The positive correlation confirms that cyclomatic complexity is independent from effects of scoring type, as intended.
“This confirms that the metric is independent from the effects of the scoring type, as intended.”
Quote in the paperNot a substantial claim - Interpretationown data
The evidence for negative effects of social comparison on effort is strongest and most consistent for low-performing individuals.
“The evidence is strongest and most consistent when it comes to the negative effects of social comparison on the effort exerted by low-performing individuals.”
Quote in the paperSubstantial claim - Study design and conductreasoning
Analyzing all individuals in matched challenges would not allow distinguishing self-selection into treatment from actual treatment effect on behavior.
“Analyzing all the individuals in the matched challenges would not allow us to distinguish between selfselection into the treatment and any actual treatment effect on behavior.”
Quote in the paperSubstantial claim - Interpretationreasoning
Relative deprivation among those at the bottom may trigger feelings of unfairness and resentment.
“This may trigger feelings of unfairness, resentment, even relative deprivation among those at the bottom of the chart.”
Quote in the paperSubstantial claim - Interpretationreasoning
Individuals who put in effort and improve personal performance may lack a sense of accomplishment because others may still advance faster.
“Individuals who put the effort and improve their personal performance may lack a sense of accomplishment because others may still be advancing faster.”
Quote in the paperSubstantial claim - Interpretationown data
The results support H1, the hypothesis that relative scoring differentially affects effort by rating.
“Overall, these results support H1.”
Quote in the paperSubstantial claim - Study design and conductreasoning
Restricting sample to coders with observations for both scoring procedures in same stratum and with prior performance measures approximates a controlled crossover experiment.
“To provide causal answers to the hypotheses, we restrict the sample of contest participants to coders with observations for both scoring procedures in the same stratum for whom there are also measures of prior performance.Doing this allows us to approximate a controlled crossover experiment in which subjects are exposed to both the treatment and control and their outcomes are measured on each occ”
Quote in the paperSubstantial claim - Premisecitation
Prior empirical research shows that higher-ranked individuals behave more competitively than lower-ranked individuals.
“Indeed, prior empirical research shows that higher-ranked individuals behave more competitively than lower-ranked individuals (Garcia, Tor, & Gonzalez, 2006).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Garcia, Tor, & Gonzalez, 2006 source
- Interpretationown data
Using social comparison as performance feedback may harm lower achievers in competitive contexts.
“Our findings suggest that relying on social comparison for performance feedback could negatively affect underachievers in competitive settings.”
Quote in the paperSubstantial claim - Interpretationown data
Even without ties to pay or promotion, performance feedback can start a self-reinforcing cycle that reduces effort among bottom performers.
“Our research suggests that even if such performance feedback is not directly related to financial rewards and promotion, it can trigger a self-reinforcing process and lower effort further among bottom performers.”
Quote in the paperSubstantial claim - Interpretationown data
Relative feedback produces a more extreme distribution of individual performance within the group.
“Thus, as predicted, relative feedback leads to individual performance that is more extremely distributed in the group.”
Quote in the paperSubstantial claim - Interpretationown data
Two of the three hypotheses received empirical support.
“The empirical results support two of our three hypotheses.”
Quote in the paperSubstantial claim - Interpretationreasoning
This may create a rich-get-richer, poor-get-poorer cycle that further widens engagement and performance gaps.
“This can trigger a richget-richer-poor-get-poorer dynamic that increases engagement and performance differences in the group even further.”
Quote in the paperSubstantial claim - Study design and conductreasoning
Matching does not fully rule out confounding.
“We also recognize that our sophisticated matching techniques do not entirely preclude the possibility for confounding.”
Quote in the paperSubstantial claim - Interpretationown data
Findings imply that gamified learning via leaderboards and badges may help students unequally when individual competition is emphasized.
“our findings suggest that this innovation may not be equally beneficial for students of all ability levels if the emphasis is on individual competition.”
Quote in the paperSubstantial claim - Interpretationown data
The study indicates that curved grading may widen effort and performance gaps between low and high achievers.
“but our research suggests that it might also increase the disparity in effort and performance between bottom achievers and top achievers.”
Quote in the paperSubstantial claim - Interpretationown data
The data do not provide sufficient evidence in support of H3, the hypothesis that relative scoring affects predictability of performance.
“In short, the data do not provide sufficient evidence in support of H3.”
Quote in the paperSubstantial claim - Study design and conductnone given
The matched sample is not an accurate representation of the Topcoder population.
“We know for a fact that the matched sample we study does not represent the Topcoder population accurately.”
Quote in the paperSubstantial claim - Premisecitation
Low and top performers appear to benefit similarly from public submissions and peer comments.
“Importantly, low and top performers appear to benefit similarly from such an arrangement (LaToza et al., 2015).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites LaToza et al., 2015 source
- Study design and conductown data
Cyclomatic complexity, used to gauge final code quality, has shortcomings.
“The measure for the quality of performance we employed-the cyclomatic complexity of the final submitted code-has limitations too.”
Quote in the paperSubstantial claim
- Classified as probably not a substantial claim. 69
- May combine two claims. 48
- The quote only partly states this claim. 2
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationWithin-group competition for scarce resources decreases contributions to public goods.
“Within-group competition for scarce resources reduces trust and trustworthiness (Charness, Masclet, & Villeval, 2013;Chowdhury & Gürtler, 2015;Liu, Lin, & Xin, 2014), decreases contributions to public goods (Barker & Barclay, 2016), diminishes cooperation (West et al., 2006), and hinders agreement in bargaining situations (Barclay & Stoller, 2014).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Barker & Barclay, 2016 source
The General discussion reports that people are less willing to contribute money in a prisoner’s dilemma game under local competition. That supports reduced contributions in a related cooperative setting, but the paper does not establish decreased contributions to public goods specifically; its own experiments measured money spent harming others. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Classified as probably not a substantial claim.
PremisecitationContests and tournaments with rank-dependent financial rewards elicit more individual effort than piece-rate remuneration schemes.
“Specifically, contests and tournaments, where financial rewards depend on rank performance, elicit more individual effort than piece-wise remuneration schemes and, in fact, drive individual effort expenditure beyond what is rational (Cason, Masters, & Sheremeta, 2010;Connelly, Tihanyi, Crook, & Gangloff, 2014;Dechenaux, Kovenock, & Sheremeta, 2015;Dickinson & Isaac, 1998).”
Quote in the paperSubstantial claim1 of 3 sources checked- cites Cason, Masters, & Sheremeta, 2010 source
The Introduction and §2.1 describe rank-dependent contests and a piece-rate option. But §3.1 and the Conclusions compare achievement chiefly between proportional-prize and winner-take-all contests; they do not establish that contests elicit more individual effort than piece-rate pay. Section 3.3 reports excessive contest entry relative to expected payoffs, not effort expenditure beyond a rational level. (LLM, conf=0.82)
Experimental — accuracy not yet measured - cites Connelly, Tihanyi, Crook, & Gangloff, 2014 source
- cites Dechenaux, Kovenock, & Sheremeta, 2015 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationContests and tournaments drive individual effort expenditure beyond what is rational.
“Specifically, contests and tournaments, where financial rewards depend on rank performance, elicit more individual effort than piece-wise remuneration schemes and, in fact, drive individual effort expenditure beyond what is rational (Cason, Masters, & Sheremeta, 2010;Connelly, Tihanyi, Crook, & Gangloff, 2014;Dechenaux, Kovenock, & Sheremeta, 2015;Dickinson & Isaac, 1998).”
Quote in the paperNot a substantial claim1 of 3 sources checked- cites Cason, Masters, & Sheremeta, 2010 source
The Introduction (p. unavailable) says the contests elicit effort, and Results §3.1 reports greater total achievement in proportional-prize than winner-take-all contests. But §3.1 says overall achievement is nearly identical when piece-rate effort is included, and §3.3 documents excessive contest entry, not individual effort expenditure beyond a rational level. The source therefore does not support the claimed excess individual effort or a general effort advantage over piece-rate pay. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites Connelly, Tihanyi, Crook, & Gangloff, 2014 source
- cites Dechenaux, Kovenock, & Sheremeta, 2015 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationWithin-group competition for scarce resources reduces trust and trustworthiness.
“Within-group competition for scarce resources reduces trust and trustworthiness (Charness, Masclet, & Villeval, 2013;Chowdhury & Gürtler, 2015;Liu, Lin, & Xin, 2014), decreases contributions to public goods (Barker & Barclay, 2016), diminishes cooperation (West et al., 2006), and hinders agreement in bargaining situations (Barclay & Stoller, 2014).”
Quote in the paperNot a substantial claim2 of 3 sources checked- cites Charness, Masclet, & Villeval, 2013
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Chowdhury & Gürtler, 2015 source
The discussion of sabotage in contests (section 4.1, p. 8) reports reduced productive effort and underreporting of rivals’ performance; the conclusion (p. 17) also notes that sabotage can occur in teams. Neither passage addresses whether within-group competition for scarce resources reduces trust or trustworthiness. Searches for those terms found no passage that does. (LLM, conf=0.72)
Experimental — accuracy not yet measured - cites Liu, Lin, & Xin, 2014 source
The Discussion reports that within-group competition reduced trustworthiness among acquaintances, but it improved trust rather than reducing it. The Abstract specifies that participants competed for financial rewards; it does not describe the rewards as scarce resources. (LLM, conf=0.92)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationWithin-group competition for scarce resources hinders agreement in bargaining situations.
“Within-group competition for scarce resources reduces trust and trustworthiness (Charness, Masclet, & Villeval, 2013;Chowdhury & Gürtler, 2015;Liu, Lin, & Xin, 2014), decreases contributions to public goods (Barker & Barclay, 2016), diminishes cooperation (West et al., 2006), and hinders agreement in bargaining situations (Barclay & Stoller, 2014).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Barclay & Stoller, 2014 source
The Results (section 3) report that participants under local competition demanded higher minimum offers in the ultimatum game, supporting greater rejection of unfair offers. That supports difficulty reaching agreement on low offers, but the paper does not establish that local competition reduced bargaining agreements overall; the claim is broader than the measured result. (LLM, conf=0.85)
Experimental — accuracy not yet measured
May combine two claims.
Study design and conductown dataBecause pruned data are used, the treatment effect cannot be claimed for whole population; estimand is local average treatment effect for treated.
“Since we are using pruned data, we cannot claim to measure the treatment effect on the whole population.Rather, the estimand in our statistical models is the local average treatment effect for the treated (Iacus et al., 2012).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Iacus et al., 2012 source
Section 6.3.3 defines the estimand after matching as the average treatment effect for the matched people, which the paper calls a “local SATT.” But Section 2.3 says pruning only control units leaves the target quantity unchanged; the shift to a local estimand depends on pruning treated units, not merely on using pruned data. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Classified as probably not a substantial claim.
Study design and conductcitationNon-balanced design with non-independent observations is accounted for by fitting mixed effects models with random intercepts for strata, challenges, and individuals.
“Essentially, we have a non-balanced design with non-independent observations and we account for it by fitting mixed effects models with random intercepts for strata, challenges, and individuals (Barr, Levy, Scheepers, & Tily, 2013).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Barr, Levy, Scheepers, & Tily, 2013 source
In “Random effects in LMEMs and ANOVA,” the paper explains that random intercepts can account for clustering and non-independence across subjects and items. It does not prescribe intercepts for the citing study’s particular strata, challenges, and individuals. In “Identifying the maximal random effects structure,” it also advises trying random slopes for unbalanced within-unit designs, so intercepts alone are not generally sufficient. (LLM, conf=0.87)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationCurved grading has been found to raise average effort in certain settings.
“Grading on a curve", whereby course grades are determined in reference to others' performance, has been shown to increase average effort under some conditions (Andreoni & Brownback, 2017;Becker & Rosen, 1992)”
Quote in the paperNot a substantial claim1 of 2 sources checkedMay combine two claims.
Findingown dataIn Topcoder contests where submitted code is scored relative to other submissions rather than on an absolute scale, top performers increase effort while bottom performers decrease effort.
“We find that in contests where the submitted code is evaluated against others' submissions, rather than using an absolute scale, top performers increase their effort while bottom performers decrease it.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataCompared to average contestant in same challenge, the subjects are more experienced and higher rated, make more submissions, and perform better.
“Compared to the average contestant in the same challenge, our "subjects" are more experienced and higher rated; further, they make more submissions and perform better (Table 5).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataUnder relative scoring, low-rated coders' performance seems more predictable, whereas those in the 60-80 rating percentile seem less predictable.
“It appears that relatively scored challenges make the performance of low-rated individuals more predictable, while that of individuals in the 60-80 percentile by rating less predictable.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataPerformance predictability tends to be higher at both the low and high ends of the rating scale under relative scoring.
“the U-shaped relationship in the right panel in Figure 2 suggests a tendency for higher predictability at the lower and higher ends of rating.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataCyclomatic complexity typically increases with subsequent submissions, supporting the idea that it reflects elaboration and development rather than consolidation and refinement.
“Relevantly to this, we find that the cyclomatic complexity typically increases with subsequent submissions, further supporting the idea that it reflects elaboration and development, rather than consolidation and refinement (Fig. 3, left).”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Findingown dataAfter matching and identifying individuals with rating who have at least one treatment and one control observation in a stratum, sample is 1,239 observations of 319 coders over 42 challenges in 12 strata; these coders made 12,914 successful code submissions.
“After matching the challenges and identifying individuals with Topcoder rating who have at least one treatment and one control observation in one of the strata, we are left with 1,239 observations of 319 coders over 42 challenges in 12 strata; these coders made 12,914 successful code submissions altogether.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAfter matching, dataset has 52 challenges in 16 strata, with reasonable covariate balance for six coarsened variables.
“After performing the matching procedure, we obtain a dataset of 52 challenges in 16 strata, with a reasonable covariate balance for the six coarsened variables (Fig. 4).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataFindings apply only to experienced coders, not to first-time contestants.
“In supplementary analyses, we found out that our results concern only coders with prior experience on the platform and do not extend to first-time contestants.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAmong 31 relative-scoring challenges, six used downward comparison based on competitors beaten; the rest used upward comparison normalized by the best score.
“Out of the 31 relatively scored challenges we analyze, in six the score depends on the number of competitors the player beats (downward comparison) while in the rest, the score is normalized by the current best score (upward comparison).”
Quote in the paperSubstantial claimThe quote only partly states this claim.
Findingown dataAmong first-time contestants, relative scoring is associated with lower code complexity in the first submission (coefficient −0.441, p<0.05).
“Relative scoring | | 0.319 | | -0.353 * | | -0.441 *”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataSplitting by comparison direction suggests downward comparison discourages bottom-ranked coders, while upward comparison encourages top-ranked coders.
“When we split the analyses by the direction of comparison, we observe that downward comparison appears to be the one that demotivates those already at the bottom of the ranking, while upward comparison is the one that motivates those at the top (Fig. 6).”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataRelative scoring produces better outcomes for top performers but lower engagement for bottom performers.
“As a result, relative scoring leads to better outcomes for those at the top but lower engagement for bottom performers.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationHumans have a powerful drive to gain accurate self-evaluations and often compare their opinions and skills with others.
“According to the eminent social psychologist Leon Festinger, we have a powerful drive to gain accurate self-evaluations and we often achieve this by comparing our opinions and skills to those of others (Festinger, 1954).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Festinger, 1954 source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationTopcoder is the largest competitive software development portal and constitutes both a marketplace for software solutions and a social network community.
“Established in 2001, Topcoder is the largest competitive software development portal that constitutes both a marketplace for software solutions and a social network community.”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationCompetition increases spiteful behavior and sabotage, making individuals more likely to harm others at personal cost to diminish opponents' performance and increase their own chances of winning.
“Competition also increases spiteful behavior and sabotage, making individuals more likely to harm others at a personal cost in order to diminish their opponents' performance and increase their own chances of winning (Harbring & Irlenbusch, 2011).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Harbring & Irlenbusch, 2011 source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationReducing experiential inequality increases individual productivity and job satisfaction and improves organizational performance.
“More generally, reducing experiential inequality has been shown to increase individual productivity and job satisfaction and improve organizational performance (Bapuji, Ertug, & Shaw, 2020;Stainback, Tomaskovic-Devey, & Skaggs, 2010).”
Quote in the paperNot a substantial claim0 of 2 sources checkedClassified as probably not a substantial claim. May combine two claims.
InterpretationreasoningSocial comparison could result in rich-get-richer and poor-get-poorer dynamics that widen existing ability differences and make performance more predictable.
“This implies that social comparison could result in a rich-get-richer and poor-get-poorer dynamics that widen existing ability differences and make performance more predictable, going directly against Festinger's arguments.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationTwo mechanisms from Festinger's arguments are empirically supported: selection into and out of contests, and sabotage of winners.
“Two of these mechanisms are empirically supported: the tendency to select into and out of contests (Dohmen & Falk, 2011;Eriksson, Teyssier, & Villeval, 2009) and the tendency to sabotage winners in contests (Chen, 2003).”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationOnline citizen science initiatives, learning platforms, user-contribution communities, and crowd workplaces flourish when their members keep engaging and learning.
“Online citizen science initiatives, learning platforms, user-contribution communities, and crowd workplaces flourish when their members keep engaging and learning (Kittur et al., 2013).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Kittur et al., 2013 source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationContests can be successfully employed to spur innovation.
“As a result, contests can be successfully employed to spur innovation (Wright, 1983).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Wright, 1983
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationTopcoder maintains an active community of hundreds of thousands of designers, developers, data scientists, and programmers who regularly compete in programming challenges.
“Topcoder maintains an active community of hundreds of thousands of designers, developers, data scientists, and programmers who regularly compete in programming challenges (Archak, 2010; K. J. Boudreau, Lacetera, & Lakhani, 2011;K. Boudreau, Helfat, Lakhani, & Menietti, 2012;Lakhani et al., 2013).”
Quote in the paperNot a substantial claim0 of 3 sources checkedMay combine two claims.
InterpretationreasoningSocial comparison may have differing effects on different people: some become more competitive, while others lose interest and decrease effort as their chances of winning drop.
“In essence, social comparison may have differing effects on different people: some may become more competitive, while others may lose interest and decrease effort as their chances of winning drop.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationInequality in heterogeneous populations has two distinct aspects: dispersion of outcomes and predictability of outcomes.
“When considering inequality in heterogeneous populations, there are two distinct aspects to take into account-the dispersion of outcomes and the predictability of outcomes (Lynn, Podolny, & Tao, 2009;Salganik, Dodds, & Watts, 2006).”
Quote in the paperSubstantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationGame design elements such as likes, badges, skill levels, and leaderboards are used to increase learning in online courses, exercise on e-health platforms, or performance in standard organizational tasks.
“This is the idea behind using game design elements such as likes, badges, skill levels, and leaderboards to increase learning in online courses, exercise on e-health platforms, or performance in standard organizational tasks (Denny, McDonald, Empson, Kelly, & Petersen, 2018;Koivisto & Hamari, 2019;Landers, Bauer, & Callan, 2017).”
Quote in the paperNot a substantial claim0 of 3 sources checkedClassified as probably not a substantial claim. May combine two claims.
PremisecitationSocial comparison can induce competitive behavior and increase effort even without financial incentives.
“Still, social comparison can induce competitive behavior and increase effort even without financial incentives (Mago, Samak, & Sheremeta, 2016;Messick & McClintock, 1968;Sheremeta, 2010).”
Quote in the paperSubstantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S2 (checkable).
Study design and conductreasoningTo measure performance quality, an objective metric independent of scoring type is required.
“To measure the quality of performance, we require an objective metric that does not depend on the scoring type.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationCyclomatic complexity is a software complexity measure based on the decision structure of code.
“Cyclomatic complexity is a software complexity measure that is based on the decision structure of the code (McCabe, 1976).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites McCabe, 1976 source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationCoders contribute to such challenges to learn and practice and to establish reputation in the community.
“Coders contribute to such challenges in order to learn and practice and in order to establish their reputation in the community (K.J. Boudreau et al., 2011;K. Boudreau et al., 2012).”
Quote in the paperNot a substantial claim0 of 2 sources checkedMay combine two claims.
Study design and conductown dataIn most cases, observations in matched data span full range of values, giving enough variability to make conclusions about a diverse set of individuals on Topcoder.
“Nevertheless, in most cases, the observations in the matched data span the full range of values, giving us enough variability to make conclusions about a diverse set of individuals on Topcoder.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductreasoningThe interaction term between scoring type and coder rating helps disentangle different effects expected on high-performers and low-performers.
“The interaction term between scoring type and coder rating b 3 X j Z ij helps to disentangle the different effects we expect on high-performers and low-performers.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataModels control for treatment ordering effects by including number of challenges the coder has already participated in.
“Additionally, we control for treatment ordering effects by including the number of challenges the coder has already participated in, which is another challenge-varying individual-level covariate (P ij ).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataThe decision to model relationships as quadratic/cubic is informed by shape of bivariate relations in Figures 1 and 2 and best model fit.
“In practice, we model the relationship between rating and proximity to rating as quadratic and the relationship between rating and code complexity, number of code lines, number of submissions, and time in challenge as cubic.This decision is informed by the shape of the bivariate relations depicted in Figure 1 and Figure 2 and the best model fit.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductreasoningGamma regression with log link is used for number of code lines, number of submissions, and code complexity because they are strictly positive, right-skewed, continuous count-like variables.
“The outcome variables number of code lines written, number of submissions, and code complexity are strictly positive and can be seen as a continuous representation of count variables.The variables are heavily skewed to the right and hence, we fit gamma regression models with a log link function for these measures (Brooks et al., 2017;Faraway, 2005).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Brooks et al., 2017
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Faraway, 2005
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningThe rating measure is noisier for coders who have participated in fewer previous challenges.
“It should be noted that this measure is not perfect since it is noisier for coders who have participated in fewer previous challenges on the platform.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataBecause data are observational, a quasi-experimental design is used to establish a causal link between scoring type and dispersion of effort, dispersion of performance, and predictability of performance.
“Since our data are observational, we use a quasi-experimental research design to establish a causal link between scoring type and dispersion of effort, dispersion of performance, and predictability of performance.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThere are two forms of social comparison: upward and downward.
“There are two forms of social comparison people can make: upward and downward (Fiske, 2011).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Fiske, 2011
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataTo confirm robustness of effort findings, the paper also examines number of submissions and duration of participation as proportion of challenge duration.
“To confirm the robustness of the findings on effort, we additionally look at the number of submissions and the duration of the coder's participation in the challenge, expressed as a proportion of the challenge duration.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataObservations are weighted by strata because not all types of challenges are equally represented.
“Since not all types of challenges are equally represented, we additionally weigh the observations by strata.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe matched challenges are designed and posted by Topcoder to keep their user community stimulated and engaged.
“This means that they are challenges designed and posted by Topcoder with the purpose to keep their user community stimulated and engaged.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductreasoningThe crossover experimental design compares difference in performance between treatment and control within individuals, ignoring individual differences in coding style and focusing on change in code complexity for each individual.
“Most importantly, the cross-over experimental design we employ compares the difference in performance between the treatment and control within individuals.Essentially, we ignore individual differences in coding style and only focus on the change in the code complexity for each individual.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationThese subjective experiences have been linked to short-sightedness, emphasis on small immediate rewards, and more risky behavior.
“These subjective experiences have been linked to short-sightedness, emphasis on small and immediate rewards compared to large and delayed ones (Callan, Shead, & Olson, 2011), and more risky behavior (Payne, 2017).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Callan, Shead, & Olson, 2011 source
- cites Payne, 2017
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataTwo cases with more than 10,000 new code lines between submissions were hard-coded data and were removed as outliers.
“There are two cases where more than 10,000 new code lines were inserted between subsequent submissions, which on closer inspection turned out to constitute hard-coded data; we remove these two outliers from the relevant analyses.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductreasoningMatching variables chosen are all variables that might be correlated with scoring type and outcomes: size, duration, difficulty, reward structure, and task.
“We choose to match on all variables that might be correlated with the scoring type X and any of the outcomes Y : variables related to the size, duration, difficulty, reward structure, and task of the challenge.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationUpward social comparison often triggers negative self-evaluation, resentment, and envy, but can also be motivating and self-enhancing.
“Upward social comparison occurs when people compare themselves to those who are better off and although it often triggers negative self-evaluation, resentment, and envy, it can also be motivating and self-enhancing (Collins, 1996).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Collins, 1996 source
Classified as probably not a substantial claim.
Study design and conductreasoningTopcoder Marathon Match rating is used as proxy for prior performance/ability.
“For prior performance, which we use as proxy for ability, we use the coder's official Topcoder Marathon Match rating.”
Quote in the paperSubstantial claim- Rests on an assumption: The Topcoder Marathon Match rating is a valid proxy for coder ability/prior performance.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningBeta regression is used for proximity to rating and time in challenge because they are positive and bounded by 1.
“The outcome variables proximity to rating and time in challenge are also positive but bounded by 1 and hence, we fit a beta regression model for these two cases.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationLines of code is a common metric for programmer productivity and effort in software development management.
“Our primary measure of effort is lines of code (LOC)-a common metric for programmer productivity and effort employed in software development management (Fenton & Neil, 2000).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Fenton & Neil, 2000 source
May combine two claims.
PremisecitationDownward social comparison generally boosts self-confidence and improves self-evaluation but may also induce negative affect, especially among those with low self-confidence.
“Downward social comparison occurs when people compare themselves to those who are worse off and, in parallel, it generally boosts self-confidence and improves self-evaluation but may also induce negative affect, especially among those with low self-confidence (Buunk, Collins, Taylor, VanYperen, & Dakof, 1990).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Buunk, Collins, Taylor, VanYperen, & Dakof, 1990 source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductcitationCyclomatic complexity increases with modularization and design effort, but very convoluted and inefficient code would also have high cyclomatic complexity.
“Although the measure increases with modularization and design effort (Shepperd, 1988), very convoluted and inefficient code would also have high cyclomatic complexity.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Shepperd, 1988 source
May combine two claims.
Study design and conductown dataThe correlation with rank percentile can be quite low in some cases, indicating the measure mainly captures elaboration and development of code rather than how well it addresses the problem.
“Nevertheless, the correlation with rank percentile can be quite low in some cases (Fig. 3, right), which brings attention to the fact that the measure mainly captures the elaboration and development of the code, rather than how well it addresses the particular problem.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataData are partially crossed because individuals are nested within different challenges but not all individuals are present in all challenges.
“Our data are partially crossed since individuals are nested within different challenges but not all individuals are present in all of the challenges.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningSocial comparison exaggerates small differences in ability and effort, but individuals' reactions to ongoing results can feed back into behavior and reinforce these differences.
“In short, social comparison exaggerates small differences in ability and effort but individuals' reaction to the ongoing results could feedback into individuals' behavior in a way that reinforces these differences.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductcitationCyclomatic complexity measures how advanced and sophisticated the coder's solution is in terms of control logic.
“We use cyclomatic complexity to measure how advanced and sophisticated the coder's solution is in terms of control logic (Munson & Khoshgoftaar, 1989).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Munson & Khoshgoftaar, 1989
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningThe limitation of noisier rating for less experienced coders is mitigated by controlling for number of previous challenges in statistical models.
“We mitigate for this limitation, however, by controlling for the number of previous challenges in the statistical models.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe LOC measure generally captures the amount of work and attention involved, though it does not differentiate small edits from major overhauls.
“This measure does not differentiate between small edits, such as changing a variable name, and a major overhaul, such as rewriting the algorithm, but it generally captures the amount of work and attention involved.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductreasoningAbsolute final scores are deliberately scaled for relatively scored challenges, and final rank percentiles are influenced by skill-based selection into differently scored challenges, so neither can be used as an objective performance metric.
“This precludes using the absolute final scores since they are deliberately scaled for relatively scored challenges, as well as the final rank percentiles since they are influenced by differences in skill-based selection into differently scored challenges.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataLeaderboards and similar feedback can create short-term client value yet also push away users, altering a community's size, makeup, and identity.
“Leaderboards, rankings, and other-based feedback may help elicit value for clients in the short term but may also drive away some users, inadvertently impacting the size, composition, and identity of the community.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataWithout chances to cooperate, social comparison can lead lower achievers to try less hard, not more, thereby increasing performance inequality.
“We found that, in the absence of opportunities for cooperation, social comparison can actually make underachievers exert less effort, not more, and thus produce more unequal performance.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataThe final matched set contained 52 challenges drawn from 49 unique problems.
“The final set of 52 matched challenges corresponds to 49 unique problems.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataMissing values arose in cyclomatic complexity and file diff analyses when algorithms failed on submitted code.
“The analyses involving cyclomatic complexity and file diffs also contain a number of missing values due to the algorithms failing to run on the submitted code.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductreasoningOnly contestants with at least one non-zero submission were included.
“Similarly, when analyzing contestants in a challenge, we only considered individuals who made at least one non-zero submission.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationMcCabe's cyclomatic complexity was used to measure code complexity.
“The complexity of the source code was measured using cyclomatic complexity, as introduced by Thomas J. McCabe (McCabe, 1976).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites McCabe, 1976 source
May combine two claims.
InterpretationcitationSocial comparison, when paired with cooperation and idea sharing, can support learning, community, and innovation.
“When combined with opportunities for cooperation and idea exchange, social comparison can in fact foster learning, community building, and innovation.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataEffort was quantified by summing newly inserted code lines across submissions.
“To quantify effort, we estimate the total number of lines the coder wrote for the challenge.To do this, we sum over the number of new code lines inserted with every submission.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductreasoningMatching and a quasi-experimental crossover design meant most data were discarded and the claims' scope was reduced.
“To provide more accurate causal estimates, we used matching and a quasiexperimental crossover design.However, this made us disregard the majority of our data and narrow down the scope of our claims.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataFive challenges with fewer than 10 contestants were also removed.
“We additionally removed any challenge that had fewer than 10 contestants (5 challenges).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationSimpleDiff, Paul Butler's implementation using the Ratcliff/Obershelp algorithm, was the diff tool.
“The particular implementation of the diff utility we used (SimpleDiff) was developed by Paul Butler and relies on the Ratcliff/Obershelp algorithm to solve the longest common subsequence problem in order to determine the smallest set of deletions and insertions to create one set from the other (Ratcliff & Metzener, 1988).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Ratcliff & Metzener, 1988
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing). The quote only partly states this claim.
Study design and conductreasoningRating analyses used 42 rather than 52 challenges because new contestants have no Topcoder rating.
“Since challenges that took place soon after Topcoder started contain mainly new platform members, the number of observations that we end up analyzing when rating is involved is actually lower: 42 instead of the 52 challenges we originally matched.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductreasoningBecause an objective performance measure uncorrelated with scoring type was needed, official challenge metrics could not be used.
“We required an objective measure of performance that is not correlated with the scoring type; we thus could not rely on the official performance metrics employed by the challenges.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductown dataThe effort and performance models weight observations by stratum, adjust for treatment ordering via the count of prior challenges, and include random intercepts for individual, challenge, and stratum.
“The models include weights by stratum and control for treatment ordering effects using the number of previous challenges the coder has participated in.The models also include random intercepts by individual, challenge, and stratum.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThe issue is not social comparison itself but how it is used to intensify competition.
“Yet, it is not social comparison per se that is the problem but its use to heighten competition.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationThe Python package Lizard computed cyclomatic complexity.
“We calculated cyclomatic complexity with the Python package Lizard (Yin, 2019).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Yin, 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
Interpretationown dataCompetition and social comparison may harm motivation and learning.
“social comparison and competition can negatively impact motivation and learning”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductreasoningThe design handles self-selection bias well but is limited regarding sample selection bias.
“Further, our research design is powerful when it comes to dealing with self-selection bias, but has limitations with respect to sample selection bias.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe study tested Festinger's effort prediction in competitive settings and found it incorrect.
“Thus, we directly addressed Festinger's prediction on effort and proved it wrong in competitive settings.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable).
Interpretationown dataPerformance-quality findings should be read only as solution elaboration and development, not as maintainability, optimization, or legibility.
“We thus caution that our results on the quality of performance should be interpreted more narrowly in terms of the extent of elaboration and development of the solution, and not in reference to other possible criteria such as software maintainability, optimization, code legibility, etc.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing).
Interpretationown dataUpward and downward social comparison may produce different effects.
“the effects could deffer for upward and downward social comparison.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe unequal effort and performance effects may, over time, create a self-reinforcing cycle that widens the gap between bottom and top performers.
“Nevertheless, over time, the unequal effects on effort and performance we discovered could lead to a self-reinforcing loop that solidifies and even increases differences between bottom and top performers.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductreasoningUnmeasured differences between relative and absolute scoring challenges could account for the effects.
“it is still possible that relatively and absolutely scored challenges differ in respects that we have not captured and that may explain the observed effects.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenTopcoder does not represent other crowdsourcing platforms or online communities broadly.
“We recognize that the Topcoder community is not representative of other crowdsourcing platforms or online communities in general.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductown dataAfter removing 111 single-problem challenges, 256 challenges covering 214 unique problems remained.
“we excluded 111 challenges that were based on a single problem (these presented a controlled experiment on the platform), leaving us with 256 challenges based on 214 unique problems.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataHalving the data after splitting prevents quantitative separation of upward and downward comparison effects.
“Because we halve the data after the split, we cannot disentangle these effects quantitatively”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown dataCyclomatic complexity of the submitted source code is a suitable performance measure, since it tends to rise across successive submissions and correlates with rank percentile by final score.
“The cyclomatic complexity of the source code is a suitable measure of performance because it generally increases in subsequent submissions (left) and is correlated with rank percentile by final score in the challenge (right).”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataRelative feedback can energize experienced high performers while discouraging learners.
“While relative feedback may stimulate experienced overachievers, it may demotivate learners.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataSocial comparison raises effort among higher performers but lowers it among lower performers.
“we presented evidence that social comparison increases effort for better-performing individuals but has exactly the opposite effects on worse-performing individuals.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningResults may not generalize to contests with monetary prizes.
“our findings do not necessarily transfer to contests that offer monetary rewards.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationPublic submissions with peer comments and evaluation in innovation contests help participants learn from others and enhance their own solutions.
“innovation contests that make submissions public and allow for peer comments and evaluation help contestants learn and incorporate ideas from others' solutions and improve the quality of their own solution”
Quote in the paperNot a substantial claim0 of 2 sources checkedClassified as probably not a substantial claim. May combine two claims.
Study design and conductown dataZero-point submissions were excluded as frequent and likely caused by minor bugs rather than effort.
“When analyzing individual submissions we ignored 0-point submissions.These occur extremely often in the data and could be explained with minor bugs in the code that break the provisional scoring system.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductown dataScoring-type labels came from Topcoder users in a dedicated platform challenge.
“The classification by scoring type was provided by Topcoder users in a dedicated challenge on the platform.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataLow-rated coders exert less effort and submit less complex solutions in relatively scored challenges involving downward social comparison, whereas high-rated coders exert more effort and submit more complex solutions in relatively scored challenges involving upward social comparison.
“Low-rated coders put less effort and produce solutions with lower cyclomatic complexity in relatively scored challenges with downward social comparison (left), while high-rated coders put more effort and produce solutions with higher cyclomatic complexity in relatively scored challenges with upward social comparison (right).”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataFinal outcomes stem from social comparison dynamics rather than from coders' prior expectations or dispositions.
“indicating that the final outcomes are not driven by the contestants' a priori expectations and dispositions but are the result of social comparison dynamics.”
Quote in the paperSubstantial claim
The authors state this as an assumption.
Social comparison is more prominent in relatively scored challenges than in absolutely scored ones.
“We assume that social comparison is more prominent in the relatively scored challenges compared to the absolutely scored ones.”
The paper relies on this without support.
In creative tasks, effort is positively correlated with but distinct from performance.
“In creative tasks, input, or effort, is positively correlated with but still distinct from output, or performance.”
The paper relies on this without support.
Previous literature has not directly studied the effects of social comparison on inequality in effort and performance.
“Previous literature has not directly studied the effects of social comparison on inequality in effort and performance but it nevertheless contains some insights and suggestive empirical evidence, albeit contradictory.”
The paper relies on this without support.
When individual achievement is measured and evaluated in reference to others, success becomes a moving target.
“When individual achievement is measured and evaluated in reference to others, success becomes a moving target.”
The paper relies on this without support.
The Topcoder Marathon Match rating is a valid proxy for coder ability/prior performance.
“For prior performance, which we use as proxy for ability, we use the coder's official Topcoder Marathon Match rating.”
The authors state this as an assumption.
Relative and absolute scoring challenges differ solely in their emphasis on social comparison.
“We assumed that the only difference between the two types of challenges is the emphasis on social comparison but we cannot entirely preclude other subtle differences responsible for alternative behavioral mechanisms.”
The authors state this as an assumption.
Newcomers select their initial challenge at random.
“Assuming that first-timers randomly choose their first challenge, we tested if the performance, effort, and likelihood to participate again are different for those who happened to enter Topcoder with a relatively scored challenge versus absolutely scored challenge.”
The paper relies on this without support.
The scoring-type labels provided by Topcoder users accurately reflect the actual scoring type.
“The classification by scoring type was provided by Topcoder users in a dedicated challenge on the platform.”
The paper relies on this without support.
Cyclomatic complexity validly measures software quality in terms of elaborated logical flow and development.
“We chose McCabe's cyclomatic complexity with the intention to quantify software quality in terms of its elaborated logical flow and development.”
Cites
A theory of social comparison processes
Festinger, L. (1954) · Human Relations · doi:10.1177/001872675400700202 · Cited by 20,926 · see citing works
Capital in the Twenty-First Century
Piketty, T. (2014) · doi:10.4159/9780674369542 · Cited by 14,437 · see citing works
Mollie, E. B., Kristensen, K., van Benthem, K. J., Magnússon, Á., Casper, W. B., Nielsen, A., Hans, J. S., Maechler, M., & Benjamin, M. B. (2017) · The R Journal · doi:10.32614/rj-2017-066 · Cited by 13,350 · see citing works
Random effects structure for confirmatory hypothesis testing: Keep it maximal
Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013) · Journal of Memory and Language · doi:10.1016/j.jml.2012.11.001 · Cited by 10,328 · see citing works
From game design elements to gamefulness: defining "gamification"
Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011) · Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments · doi:10.1145/2181037.2181040 · Cited by 8,402 · see citing works
McCabe, T. J. (1976) · IEEE Transactions on Software Engineering · doi:10.1109/tse.1976.233837 · Cited by 6,059 · see citing works
Rank-Order Tournaments as Optimum Labor Contracts
Lazear, E. P., & Rosen, S. (1981) · Journal of Political Economy · doi:10.1086/261010 · Cited by 4,711 · see citing works
Causal Inference without Balance Checking: Coarsened Exact Matching
Iacus, S. M., King, G., & Porro, G. (2012) · Political Analysis · doi:10.1093/pan/mpr013 · Cited by 3,563 · see citing works
Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market
Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006) · Science · doi:10.1126/science.1121066 · Cited by 2,219 · cited 2× · see citing works
“When considering inequality in heterogeneous populations, there are two distinct aspects to take into account—the dispersion of outcomes and the predictability of outcomes [57, 71].”
Gamifying learning experiences: Practical implications and outcomes
Domínguez‐Díaz, A., Saenz-de-Navarrete, J., de‐Marcos, L., Sanz, L. F., Pagés, C., & Martínez-Herráiz, J.-J. (2013) · Computers & Education · doi:10.1016/j.compedu.2012.12.020 · Cited by 2,189 · see citing works
Intrinsic motivation and extrinsic incentives jointly predict performance: A 40-year meta-analysis.
Cerasoli, C. P., Nicklin, J. M., & Ford, M. T. (2014) · Psychological Bulletin · doi:10.1037/a0035661 · Cited by 1,981 · cited 1× · see citing works
“In those cases, financial considerations could overwhelm the subtle behavioral effects from social comparison we found but could also possibly interact with them [21, 55, 70].”
The Rise of Motivational Information Systems: A Review of Gamification Research
Koivisto, J., & Hamari, J. (2017) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.10.013 · Cited by 1,944 · see citing works
Hanus, M. D., & Fox, J. (2015) · Computers & Education · doi:10.1016/j.compedu.2014.08.019 · Cited by 1,918 · cited 1× · see citing works
“For example, there is a trend to use leaderboards and badges to gamify learning experiences but, complementing other empirical research [34, 43], our findings suggest that this innovation may not be equally beneficial for students of all ability levels if the emphasis is on individual competition.”
A better lemon squeezer? maximum-likelihood regression with beta-distributed dependent variables.
Smithson, M., & Verkuilen, J. (2006) · Psychological Methods · doi:10.1037/1082-989x.11.1.54 · Cited by 1,788 · see citing works
For better or worse: The impact of upward social comparison on self-evaluations
Collins, R. L. (1996) · Psychological Bulletin · doi:10.1037/0033-2909.119.1.51 · Cited by 1,166 · cited 1× · see citing works
“Upward social comparison occurs when people compare themselves to those who are better off and although it often triggers negative self-evaluation, resentment, and envy, it can also be motivating and selfenhancing [25].”
Allison, P. D. (1978) · American Sociological Review · doi:10.2307/2094626 · Cited by 1,133 · see citing works
Motivational bases of choice in experimental games
Messick, D. M., & McClintock, C. G. (1968) · Journal of Experimental Social Psychology · doi:10.1016/0022-1031(68)90046-2 · Cited by 1,097 · see citing works
Kittur, A., Nickerson, J. V., Bernstein, M., Gerber, E., Shaw, A., Zimmerman, J., Lease, M., & Horton, J. (2013) · Proceedings of the 2013 conference on Computer supported cooperative work · doi:10.1145/2441776.2441923 · Cited by 1,034 · see citing works
The affective consequences of social comparison: Either direction has its ups and downs.
Buunk, B. P., Collins, R. L., Taylor, S. E., VanYperen, N. W., & Dakof, G. A. (1990) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.59.6.1238 · Cited by 851 · see citing works
Incentives and Problem Uncertainty in Innovation Contests: An Empirical Analysis
Boudreau, K. J., Lacetera, N., & Lakhani, K. R. (2011) · Management Science · doi:10.1287/mnsc.1110.1322 · Cited by 830 · see citing works
Van de Werfhorst, H. G., & Mijs, J. J. B. (2010) · Annual Review of Sociology · doi:10.1146/annurev.soc.012809.102538 · Cited by 801 · see citing works
Performance Pay and Multidimensional Sorting: Productivity, Preferences, and Gender
Dohmen, T., & Falk, A. (2011) · American Economic Review · doi:10.1257/aer.101.2.556 · Cited by 739 · see citing works
A survey of experimental research on contests, all-pay auctions and tournaments
Dechenaux, E., Kovenock, D., & Sheremeta, R. M. (2015) · Experimental Economics · doi:10.1007/s10683-014-9421-0 · Cited by 706 · see citing works
Distributive Justice, Equity, and Equality
Cook, K. S., & Hegtvedt, K. A. (1983) · Annual Review of Sociology · doi:10.1146/annurev.so.09.080183.001245 · Cited by 558 · see citing works
The Psychology of Competition: A Social Comparison Perspective
Garcia, S. M., Tor, A., & Schiff, T. M. (2013) · Perspectives on Psychological Science · doi:10.1177/1745691613504114 · Cited by 520 · see citing works
The Dark Side of Competition for Status
Charness, G., Masclet, D., & Villeval, M. C. (2014) · Management Science · doi:10.1287/mnsc.2013.1747 · Cited by 510 · see citing works
Gamification of task performance with leaderboards: A goal setting experiment
Landers, R. N., Bauer, K. N., & Callan, R. C. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2015.08.008 · Cited by 474 · cited 1× · see citing works
“This is the idea behind using game design elements such as likes, badges, skill levels, and leaderboards to increase learning in online courses, exercise on e-health platforms, or performance in standard organizational tasks [29, 50, 52].”
(1983) · American Economic Review · Cited by 437 · see citing works
Personal relative deprivation, delay discounting, and gambling.
Callan, M. J., Shead, N. W., & Olson, J. M. (2011) · Journal of Personality and Social Psychology · doi:10.1037/a0024778 · Cited by 364 · see citing works
Tournament Theory: Thirty Years of Contests and Competitions
Connelly, B. L., Tihanyi, L., Crook, T. R., & Gangloff, K. A. (2014) · Journal of Management · doi:10.1177/0149206313498902 · Cited by 338 · see citing works
Community‐Based Innovation Contests: Where Competition Meets Cooperation
Bullinger, A. C., Neyer, A., Rass, M., & Moeslein, K. M. (2010) · Creativity and Innovation Management · doi:10.1111/j.1467-8691.2010.00565.x · Cited by 336 · see citing works
Fenton, N. E., & Neil, M. (2000) · Proceedings of the Conference on The Future of Software Engineering · doi:10.1145/336512.336588 · Cited by 317 · see citing works
Gamified crowdsourcing: Conceptualization, literature review, and future agenda
Morschheuser, B., Hamari, J., Koivisto, J., & Maedche, A. (2017) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2017.04.005 · Cited by 309 · see citing works
Crowdsourcing and all-pay auctions
DiPalantino, D., & Vojnović, M. (2009) · ACM Conference on Electronic Commerce (EC) · doi:10.1145/1566374.1566392 · Cited by 298 · see citing works
Experimental comparison of multi-stage and one-stage contests
Sheremeta, R. M. (2010) · Games and Economic Behavior · doi:10.1016/j.geb.2009.08.001 · Cited by 297 · see citing works
Organizational Approaches to Inequality: Inertia, Relative Power, and Environments
Stainback, K., Tomaskovic-Devey, D., & Skaggs, S. (2010) · Annual Review of Sociology · doi:10.1146/annurev-soc-070308-120014 · Cited by 293 · see citing works
Incentive systems in a real effort experiment
van Dijk, F., Sonnemans, J., & van Winden, F. (2001) · European Economic Review · doi:10.1016/s0014-2921(00)00056-8 · Cited by 281 · see citing works
Organizations and Societal Economic Inequality: A Review and Way Forward
Bapuji, H., Ertug, G., & Shaw, J. D. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0029 · Cited by 277 · see citing works
Entry into winner-take-all and proportional-prize contests: An experimental study
Cason, T. N., Masters, W. A., & Sheremeta, R. M. (2010) · Journal of Public Economics · doi:10.1016/j.jpubeco.2010.05.006 · Cited by 257 · see citing works
Ranks and Rivals: A Theory of Competition
Garcia, S. M., Tor, A., & Gonzalez, R. (2006) · Personality and Social Psychology Bulletin · doi:10.1177/0146167206287640 · Cited by 252 · see citing works
Sabotage in Promotion Tournaments
Chen, K.-P. (2003) · Journal of Law, Economics, and Organization · doi:10.1093/jleo/19.1.119 · Cited by 246 · see citing works
A critique of cyclomatic complexity as a software metric
Shepperd, M. (1988) · Software Engineering Journal · doi:10.1049/sej.1988.0003 · Cited by 240 · see citing works
Sabotage in Tournaments: Evidence from a Laboratory Experiment
Harbring, C., & Irlenbusch, B. (2011) · Management Science · doi:10.1287/mnsc.1100.1296 · Cited by 239 · see citing works
Innovation Contests: A Review, Classification and Outlook
Adamczyk, S., Bullinger, A. C., & Möslein, K. M. (2012) · Creativity and Innovation Management · doi:10.1111/caim.12003 · Cited by 231 · see citing works
Feng, Y., Ye, H. J., Yu, Y., Yang, C., & Cui, T. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.12.018 · Cited by 224 · see citing works
A Sociological (De)Construction of the Relationship between Status and Quality
Lynn, F. B., Podolny, J. M., & Tao, L. (2009) · American Journal of Sociology · doi:10.1086/603537 · Cited by 210 · see citing works
Archak, N. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772694 · Cited by 187 · see citing works
Envy up, scorn down: How status divides us
Fiske, S. T. (2012) · PsycEXTRA Dataset · doi:10.1037/e669402012-009 · Cited by 181 · see citing works
Cooperation and the Scale of Competition in Humans
West, S. A., Gardner, A., Shuker, D. M., Reynolds, T., Burton-Chellow, M., Sykes, E. M., Guinnee, M. A., & Griffin, A. S. (2006) · Current Biology · doi:10.1016/j.cub.2006.03.069 · Cited by 172 · see citing works
Sabotage in contests: a survey
Chowdhury, S. M., & Gürtler, O. (2015) · Public Choice · doi:10.1007/s11127-015-0264-9 · Cited by 149 · see citing works
SELF‐SELECTION AND THE EFFICIENCY OF TOURNAMENTS
ERIKSSON, T., TEYSSIER, S., & VILLEVAL, M. (2009) · Economic Inquiry · doi:10.1111/j.1465-7295.2007.00094.x · Cited by 142 · see citing works
Empirical Support for a Causal Relationship Between Gamification and Learning Outcomes
Denny, P., McDonald, F., Empson, R., Kelly, P., & Petersen, A. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173885 · Cited by 135 · see citing works
Boudreau, K. J., Lakhani, K. R., & Menietti, M. (2016) · The RAND Journal of Economics · doi:10.1111/1756-2171.12121 · Cited by 134 · see citing works
Prize-based contests can provide solutions to computational biology problems
Lakhani, K. R., Boudreau, K. J., Loh, P., Bäckström, L., Baldwin, C. Y., Lonstein, E., Lydon, M., MacCormack, A., Arnaout, R., & Guinan, E. C. (2013) · Nature Biotechnology · doi:10.1038/nbt.2495 · Cited by 130 · see citing works
Liang, H., Wang, M., Wang, J., & Xue, Y. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.11.040 · Cited by 130 · see citing works
Bockstedt, J., Druehl, C., & Mishra, A. (2016) · Production and Operations Management · doi:10.1111/poms.12552 · Cited by 111 · see citing works
The learning effect of assessment and evaluation in high school
Becker, W. E., & Rosen, S. (1992) · Economics of Education Review · doi:10.1016/0272-7757(92)90002-k · Cited by 108 · see citing works
Mago, S. D., Samak, A. C., & Sheremeta, R. M. (2014) · Journal of Conflict Resolution · doi:10.1177/0022002714545333 · Cited by 93 · see citing works
Experimental Study of Informal Rewards in Peer Production
Restivo, M., & van de Rijt, A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0034358 · Cited by 90 · see citing works
Experimental Comparison of Multi-Stage and One-Stage ContestsPreprint
Sheremeta, R. M. (2009) · SSRN Electronic Journal · doi:10.2139/ssrn.1430947 · Cited by 81 · see citing works
Jasso, G. (1983) · Social Psychology Quarterly · doi:10.2307/3033790 · Cited by 68 · see citing works
Competing or aiming to be average?: normification as a means of engaging digital volunteers
Preist, C., Massung, E., & Coyle, D. (2014) · Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing · doi:10.1145/2531602.2531615 · Cited by 65 · see citing works
Bailey, B. P., & Horvitz, E. (2010) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/1753326.1753641 · Cited by 62 · see citing works
Capital in the Twenty-First Century
Carter, C. C. (2016) · Journal of Real Estate Literature · doi:10.1080/10835547.2016.12090422 · Cited by 57 · see citing works
Performance Pay and Multidimensional Sorting - Productivity, Preferences and Gender
Dohmen, T., & Falk, A. (2011) · RePEc: Research Papers in Economics · doi:10.5282/ubm/epub.13194 · Cited by 53
The Dimensionality Of Program Complexity
Munson, J. C., & Khoshgoftaar, T. M. (2005) · Proceedings/Proceedings - International Conference on Software Engineering · doi:10.1109/icse.1989.714426 · Cited by 48 · see citing works
Stewart, S. M., Gruys, M. L., & Storm, M. (2010) · Journal of Management & Organization · doi:10.5172/jmo.16.1.168 · Cited by 38 · see citing works
Putting the Pieces Back Together Again
Malone, T. W., Nickerson, J. V., Laubacher, R., Fisher, L. H., Boer, P. D., Han, Y., & Towne, W. B. (2017) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2998181.2998343 · Cited by 33 · see citing works
How (not) to Incent Crowd Workers
Straub, T., Gimpel, H., Teschner, F., & Weinhardt, C. (2015) · Business & Information Systems Engineering · doi:10.1007/s12599-015-0384-2 · Cited by 30 · see citing works
Local competition sparks concerns for fairness in the ultimatum game
Barclay, P., & Stoller, B. (2014) · Biology Letters · doi:10.1098/rsbl.2014.0213 · Cited by 29 · see citing works
Travis, J. P. (2008) · Science · doi:10.1126/science.319.5871.1750 · Cited by 26 · see citing works
Local competition increases people's willingness to harm others
Barker, J. L., & Barclay, P. (2016) · Evolution and Human Behavior · doi:10.1016/j.evolhumbehav.2016.02.001 · Cited by 26 · see citing works
Laboratory experiments in corporate and investment finance: a survey
Cadsby, C. B., & Maynes, E. (1998) · Managerial and Decision Economics · doi:10.1002/(sici)1099-1468(199806/08)19:4/5 · Cited by 25 · see citing works
Field Evidence on Individual Behavior & Performance in Rank-Order Tournaments
Boudreau, K. J., Helfat, C. E., Lakhani, K. R., & Menietti, M. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.2127310 · Cited by 24 · see citing works
Borrowing from the Crowd: A Study of Recombination in Software Design Competitions
LaToza, T. D., Chen, M., Jiang, L., Zhao, M., & Hoek, A. V. D. (2015) · 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering · doi:10.1109/icse.2015.72 · Cited by 23 · see citing works
All pay auctions and group size: Grading on a curve and other applications
Andreoni, J., & Brownback, A. (2017) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2017.03.017 · Cited by 18 · see citing works
Liu, G., Lin, C., & Xin, Z. (2014) · PLoS ONE · doi:10.1371/journal.pone.0103074 · Cited by 10 · see citing works
How Features of a Civic Design Competition Influences the Collective Understanding of a Problem
McInnis, B. J., Xu, X. X., & Dow, S. P. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274389 · Cited by 7 · see citing works
Tsvetkova, M., Müller, S., Vuculescu, O., et al. (2022) · figshare · doi:10.6084/m9.figshare.20292279.v1
Self-citation
Flagged first, then by citations
Research works printed but not linked: 7
- Nikolay Archak and Arun Sundararajan . 2009 . Optimal design of crowdsourcing contests . ICIS 2009 Proceedings , Vol. 200 (2009), 1--16. Nikolay Archak and Arun Sundararajan. 2009. Optimal design of crowdsourcing contests. ICIS 2009 Proceedings, Vol. 200 (2009), 1--16.
- Julian J. Faraway . 2005. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models 1 edition ed.) . Chapman and Hall/CRC , Boca Raton . Julian J. Faraway. 2005. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models 1 edition ed.). Chapman and Hall/CRC, Boca Raton.
- Jeff Howe . 2008 . Crowdsourcing: How the Power of the Crowd is Driving the Future of Business . Random House, London , UK. Jeff Howe. 2008. Crowdsourcing: How the Power of the Crowd is Driving the Future of Business. Random House, London, UK.
- James Wayne Hunt and M. Douglas MacIlroy . 1976. An Algorithm for Differential File Comparison. Bell Laboratories , Murray Hill, NJ . James Wayne Hunt and M. Douglas MacIlroy. 1976. An Algorithm for Differential File Comparison. Bell Laboratories, Murray Hill, NJ.
- B. Keith Payne . 2017. The Broken Ladder: How Inequality Affects the Way We Think, Live, and Die . Viking , New York . B. Keith Payne. 2017. The Broken Ladder: How Inequality Affects the Way We Think, Live, and Die. Viking, New York.
- John W. Ratcliff and David E. Metzener . 1988 . Pattern-matching-the gestalt approach . Dr Dobbs Journal , Vol. 13 , 7 (1988), 46 . John W. Ratcliff and David E. Metzener. 1988. Pattern-matching-the gestalt approach. Dr Dobbs Journal, Vol. 13, 7 (1988), 46.
- Jakob Rogstadius , Vassilis Kostakos , Aniket Kittur , Boris Smus , Jim Laredo , and Maja Vukovic . 2011 . An assessment of intrinsic and extrinsic motivation on task performance in crowdsourcing markets . In Fifth International AAAI Conference on Weblogs and Social Media. Association for the Advancement of Artificial Intelligence , Palo Alto, CA, 321--328. Jakob Rogstadius, Vassilis Kostakos, Aniket Kittur, Boris Smus, Jim Laredo, and Maja Vukovic. 2011. An assessment of intrinsic and extrinsic motivation on task performance in crowdsourcing markets. In Fifth International AAAI Conference on Weblogs and Social Media. Association for the Advancement of Artificial Intelligence, Palo Alto, CA, 321--328.
Cited by
Sorted by citations
Pickal, A. J., Stadler, M., Sailer, M., Bai, S., Ninaus, M., Greiff, S., Becker, N., & Koch, M. (2025) · Learning and Individual Differences · doi:10.1016/j.lindif.2025.102836 · Cited by 7 · see citing works
Hegazy, S., Elsner, C., Bosch, J., & Olsson, H. H. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112550 · Cited by 6 · see citing works
Gamification Techniques and Contribution Filtering in Crowdsourcing Micro-Task Applications
Amorim, A. M., Ribeiro, A., Arouca, M. G., Meijon, I., Cavalheiro, V., Pestana, M. C., & Vieira, V. (2024) · Journal on Interactive Systems · doi:10.5753/jis.2024.3727 · Cited by 2 · see citing works
Xu, B., Xu, K., Zhu, Y., Zhou, Y., & Liu, X. (2025) · PLoS ONE · doi:10.1371/journal.pone.0327482 · Cited by 1 · see citing works
Benefiting From Top Contestants: The Role of Seeker Feedback in Crowdsourcing Contests
Wang, M., Zhang, C., & Wang, B. (2025) · International Journal of Electronic Commerce · doi:10.1080/10864415.2025.2594329 · Cited by 1 · see citing works
Alghasham, M., Alzakan, M., & Al-Hagery, M. A. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140486 · Cited by 1 · see citing works
“While [10] examines the effects of working solo as a developer or in teams on performance and learning, the authors of [11] study the effects of game elements that exist in several crowdsourcing platforms on individual contributors.”
BackgroundThe Consensus Trap: Dissecting Subjectivity and the “Ground Truth” Illusion in Data Annotation
Munir, S., Mah, B., Kalsi, K., Kapania, S., Posada, J., Law, E., Wang, D., & Ahmed, S. I. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3806510
- Authors
- Tsvetkova, M., Müller, S., Vuculescu, O., Ham, H., & Sergeev, R. A.
- Affiliations
- London School of Economics and Political Science (GB)
- Aarhus University (DK)
- Harvard University (US)
- Venue
- Proceedings of the ACM on Human-Computer Interaction
- Published
- 2022-11-07
- Volume
- 6
- Issue
- CSCW2
- Pages
- 1-27
- ISSN
- 2573-0142 (ISSN-L)
- OpenAlex
- W4309618420
- Semantic Scholar
- 2a74399c474849148a628bd21681b6446cbc9b24
- Cited by
- 8
- References
- 79
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 1 · 2023: 1 · 2022: 1
- FWCI
- 1.43
- Type
- journal article
- Language
- en
- Open access
- green
- orcid.org
- Locations
- Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
- London School of Economics and Political Science Theses Online (London School of Economics and Political Science) (acceptedVersion)
- London School of Economics and Political Science Research Online (London School of Economics and Political Science) (acceptedVersion)
- Funders
- Volkswagen Foundation (92 173)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Physical Sciences › Computer Science › Computer Science Applications › Open Source Software Innovations
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Games and Gamification
- Keywords
- social comparison, crowdsourcing contests, ability differences, gamification, online communities, TopCoder, programming competition, user engagement
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-07.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-07.
Cited sources are checked by gpt-6-sol.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review