The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index; 4 of the 17 sources its claims cite were checked for support.
A large-scale analysis of racial disparities in police stops across the United States
Open accessAbstract
We assessed racial disparities in policing in the United States by compiling and analysing a dataset detailing nearly 100 million traffic stops conducted across the country. We found that black drivers were less likely to be stopped after sunset, when a 'veil of darkness' masks one's race, suggesting bias in stop decisions. Furthermore, by examining the rate at which stopped drivers were searched and the likelihood that searches turned up contraband, we found evidence that the bar for searching black and Hispanic drivers was lower than that for searching white drivers. Finally, we found that legalization of recreational marijuana reduced the number of searches of white, black and Hispanic drivers-but the bar for searching black and Hispanic drivers was still lower than that for white drivers post-legalization. Our results indicate that police stops and search decisions suffer from persistent racial bias and point to the value of policy interventions to mitigate these disparities.
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Claims the paper makes
38 Own result · 17 Own method · 14 Own interpretation · 8 Sources not found · 3 Partly supported · 3 No basis given · 2 No full text of the sources · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“it suffers from the problem of infra-marginality 8,26,27 .”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
What the sources say
- Supportedcites [8] Anwar, S. & Fang, H. An alternative test of racial prejudice in motor vehicle searches: theory and evidence. Am. Econ. Rev. 96, 127–151 (2006). sourceExperimental — accuracy not yet measured
The Introduction (p. 4) explicitly identifies infra-marginality as a problem for the outcome test: researchers observe average search success rates, not the marginal rates the test requires. It notes that the problem limits empirical applications of the test. (LLM, conf=0.93)
- Not checked — no full text of the sourcecites [26] Ayres, I. Outcome tests of racial disparities in police practices. Justice Res. Policy 4, 131–142 (2002). source
- Partly supportedcites [27] Knowles, J., Persico, N. & Todd, P. Racial bias in motor vehicle searches: theory and evidence. J. Polit. Econ. 109, 203–229 (2001). sourceExperimental — accuracy not yet measured
Section 2.3 (page unavailable) explains the infra-marginality problem: when guilt probabilities are exogenous, observed average guilt rates do not identify the marginal motorists relevant to a test for prejudice. But the paper distinguishes that case from its own outcome test, which relies on motorists responding to search probabilities. The unqualified claim omits that distinction. (LLM, conf=0.87)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the threshold test indicates that the bar for searching black and Hispanic drivers is generally lower than that for searching white drivers across the municipal police departments and states we consider.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Search policies do not differ within the geographic subdivisions considered.
- Rests on an assumption: Officers do not disproportionately suspect more serious criminal activity when searching black and Hispanic drivers than when searching white drivers.
- Rests on an assumption: The Bayesian threshold model's functional form and prior distributions are adequate for identifying search thresholds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In all cases, we found qualitatively similarly results that are suggestive of racial bias against black drivers in stop decisions”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In aggregate across cities, the inferred threshold for white drivers is 10.0% compared to 5.0 and 4.6% for black and Hispanic drivers, respectively.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“We note that these results are consistent with self-reported stop rates by white, black and Hispanic drivers who participated in the national PPCS 1 .”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
What the sources say
- Not matchedcites [1] Davis, E., Whyde, A.& Langton, L. Contacts between Police and the Public, 2015 (Bureau of Justice Statistics, 2018); http://www.bjs.gov/index.cfm?ty=pbdetail&iid=6406
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We find α s = -0.033(95% CI [-0.039, -0.027], P < 0.001) and α c = -0.039(95% CI [-0.045, -0.033], P < 0.001)”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In these jurisdictions, stopped black and Hispanic drivers were searched about twice as often as stopped white drivers.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For each time period, the plot shows a marked drop in the proportion of drivers stopped after dusk who are black, suggestive of discrimination in stop decisions.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We find α s = -0.033(95% CI [-0.039, -0.027], P < 0.001) and α c = -0.039(95% CI [-0.045, -0.033], P < 0.001)”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Among stopped drivers, we found that black and Hispanic individuals were, on average, searched more often than white individuals.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 17
- It may state two separate claims as one. 38
- The quote only partly states this claim. 2
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“the threshold test has been proposed as a more robust means for detecting discrimination 7,22 .”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
What the sources say
- Supportedcites [7] Simoiu, C., Corbett-Davies, S. & Goel, S. The problem of infra-marginality in outcome tests for discrimination. Ann. Appl. Stat. 11, 1193–1216 (2017). source
The Introduction describes the threshold test as a new test for discrimination that mitigates theoretical limitations of outcome analysis. The Conclusion likewise says it addresses shortcomings of the outcome test, supporting the comparison in the claim. (LLM, conf=0.93)
- Not matchedcites [22] Pierson, E., Corbett-Davies, S. & Goel, S. In International Conference on Artificial Intelligence and Statistics (eds Storkey, A. & Fernando Perez-Cruz, F.) 96–105 (PMLR, 2018).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“thresholds cannot be identified by the observed data alone 7 , and so inferences are dependent on the specific functional form of the underlying Bayesian model, including the prior distributions.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
What the sources say
- Partly supportedcites [7] Simoiu, C., Corbett-Davies, S. & Goel, S. The problem of infra-marginality in outcome tests for discrimination. Ann. Appl. Stat. 11, 1193–1216 (2017). sourceExperimental — accuracy not yet measured
Section 2.3, “Inferring search thresholds” (page unavailable), specifies a Bayesian model with priors, supporting the claim that inference uses modeling assumptions. But a passage reproduced under “Conclusion” says the model would be identified by priors rather than data *without* certain constraints; it does not say thresholds cannot be identified from observed data under the specified model. The paper also says its results are robust to the exact choice of priors, qualifying the claimed dependence on them. (LLM, conf=0.84)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The Bayesian threshold model's functional form and prior distributions are adequate for identifying search thresholds.
- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“in the six municipal police departments we consider, the search rates were 9.5% (95% CI 9.4-9.6%)for black drivers, 7.2% (95% CI 7.0-7.3%)for Hispanic drivers and 3.9% (95% CI 3.8-3.9%)for white drivers.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the gap in hit rates between white and Hispanic drivers was 7.6% (95% CI 6.7-8.6%,P < 0.001), and the gap in hit rates between white and black drivers was 2.6% (95% CI 1.9-3.4%,P < 0.001).”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The estimated gaps in search thresholds between white and non-white drivers are large and statistically significant: the 95% credible interval for the Hispanic-white difference is (-6.4,-4.4%) and the corresponding interval for the black-white difference is (-6.1, -4.0%).”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Aggregating across state patrol stops, contraband was found in 32.0% (95% CI 31.6-32.4%) of searches of white drivers compared to 24.3% (95% CI 23.5-25.2%) of searches of Hispanic drivers and 29.4% (95% CI 28.7-30.0%) of searches of black drivers.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“aggregating across municipal police departments, contraband was found in 18.2% (95% CI 17.8-18.7%) of searches of white drivers compared to 11.0% (95% CI 10.6-11.5%) of searches of Hispanic drivers and 13.9% (95% CI 13.7-14.2%) of searches of black drivers.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The gap in municipal search rates between black and white drivers was 5.6% (95% CI 5.5-5.7%,P <0.001) and the gap in search rates between Hispanic and white drivers was 3.3% (95% CI 3.1-3.5%,P <0.001).”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Similarly across states, the inferred threshold for white drivers is 20.9% compared to 16.0% for black drivers and 13.9% for Hispanic drivers.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“among state patrol stops, the annual per-capita stop rate for black drivers was 0.10 compared to 0.07 for white drivers; and among municipal police stops, the annual per-capita stop rate for black drivers was 0.20 compared to 0.14 for white drivers.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the gap in search rates between black and white drivers was 2.4% (95% CI 2.3-2.5%,P < 0.001) and the gap in search rates between Hispanic and white drivers was 2.2% (95% CI 2.1-2.2%,P < 0.001).”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For Hispanic drivers, however, we found that stop rates were lower than for white drivers: 0.05 for stops conducted by state patrol (compared to 0.07 for white drivers) and 0.09 for those conducted by municipal police departments (compared to 0.14 for white drivers).”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the eight state patrol agencies we consider, search rates were 4.3% (95% CI 4.2-4.4%)for black drivers, 4.1% (95% CI 4.0-4.1%)for Hispanic drivers and 1.9% (95% CI 1.9-1.9%)for white drivers.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the gap in hit rates between white and Hispanic drivers was 7.2% (95% CI 6.6-7.8%,P < 0.001) and the gap in hit rates between white and black drivers was 4.3% (95% CI 3.8-4.8%,P < 0.001).”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our primary dataset thus consists of approximately 95 million stops from 21 state patrol agencies and 35 municipal police departments”
- Also stated in: § Data collection and standardization. · plain
“Our primary dataset includes 94,778,505 stops from 21 state patrol agencies and 35 municipal police departments.”
§ A large-scale analysis of racial disparities in police stops across the United States > Compiling a national database of traffic stops.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one. The quote only partly states this claim.
- Also stated in: § Data collection and standardization. · plain
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“the policy change reduced the absolute gap in search rates between race groups; however, the relative gap persisted, with black and Hispanic drivers still more likely to be searched than white drivers post-legalization.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These differences are again large and statistically significant: the 95% credible interval for the Hispanic-white gap is (-8.4,-5.6%); and the analogous interval for the black-white gap is (-6.5%, -3.1%).”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the proportion of stops resulting in a search fell substantially in both Colorado and Washington, while in 12 states where marijuana was not legalized, search rates did not show sharp drops at the end of 2012.”
§ A large-scale analysis of racial disparities in police stops across the United States > Effects of legalization of marijuana.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Despite marijuana legalization decreasing search rates for white, black and Hispanic drivers, Fig. 4 shows that the relative disparity between white and non-white drivers remains.”
§ A large-scale analysis of racial disparities in police stops across the United States > Effects of legalization of marijuana.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Supplementary Fig. 3 shows that white drivers faced consistently higher search thresholds than black and Hispanic drivers, both before and after marijuana legalization.”
§ A large-scale analysis of racial disparities in police stops across the United States > Effects of legalization of marijuana.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: The latent signal distributions for each race and county are homoskedastic discriminant distributions.
- The authors state this as an assumption: The parameters φ and δ can be decomposed into additive race and location terms.
- The authors state this as an assumption: For each stop, a signal is drawn from the race-and-location-specific distribution; a search occurs if the signal exceeds the race-and-location-specific threshold; if searched, contraband is found with probability equal to the signal.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For all three depicted time windows, black drivers comprise a smaller share of stopped drivers after dark, when a veil of darkness masks their race, suggestive of racial profiling.”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The proportion of drivers with typically Hispanic names labelled as Hispanic in the raw data was 70% in Colorado and 79% in Arizona, much higher than in Texas.”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection and standardization.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Despite these lower inferred search thresholds, hit rates for black drivers are comparable to those for white drivers, ostensibly due to the problem of infra-marginality in outcome tests.”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Supplementary Fig. 3 also shows that the threshold faced by all groups decreases after legalization (although not all drops are statistically significant).”
§ A large-scale analysis of racial disparities in police stops across the United States > Effects of legalization of marijuana.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found that α race r I is large and negative for white, black and Hispanic drivers, suggesting that the observed drop in searches in Colorado and Washington was due to marijuana legalization in those states.”
§ A large-scale analysis of racial disparities in police stops across the United States > Effects of legalization of marijuana.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: For each stop, a signal is drawn from the race-and-location-specific distribution; a search occurs if the signal exceeds the race-and-location-specific threshold; if searched, contraband is found with probability equal to the signal.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The outcome test thus indicates that search decisions may be biased against Hispanic drivers, but the evidence is more ambiguous for black drivers.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: Officers exercise discretion in whom to search.
- The authors state this as an assumption: In the absence of discrimination, searched minority drivers should be found with contraband at the same rate as searched white drivers.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“suggesting discrimination among both state patrols and municipal police departments.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one. The quote only partly states this claim.
- The authors state this as an assumption: Officers who engage in racial profiling are less able to identify a driver's race after dark than during the day.
- Rests on an assumption: Race-specific driving behaviour is not related more to lighting than to time of day.
- Rests on an assumption: Artificial lighting does not substantially weaken the relationship between sunlight and visibility.
- Rests on an assumption: Violation type is not related to lighting in a way that biases the veil-of-darkness test.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The data thus suggest that, although overall search rates dropped in Washington and Colorado, black and Hispanic drivers still faced discrimination in search decisions.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Search policies do not differ within the geographic subdivisions considered.
- Rests on an assumption: Officers do not disproportionately suspect more serious criminal activity when searching black and Hispanic drivers than when searching white drivers.
- Rests on an assumption: The Bayesian threshold model's functional form and prior distributions are adequate for identifying search thresholds.
In the paper
“past work revealed that Texas State Patrol officers incorrectly recorded many Hispanic drivers as white, an error the agency subsequently corrected 36 .”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection and standardization.
What the sources say
- Not matchedcites [36] Friberg, B. et al. Texas Troopers Ticketing Hispanic Drivers as White (KXAN, 2015); https://www.kxan.com/investigations/texas-troopers-ticketing-hispanic-drivers-as-white/
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We used all available data that were sufficiently complete to conduct our analysis.”
§ A large-scale analysis of racial disparities in police stops across the United States > Additional information
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The raw data we received from the states and cities, the processed data we used in this analysis and the code to clean and analyse the data are all available for public inspection and reuse.”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection and standardization.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The threshold test for discrimination was introduced by Simoiu et al. 7 to mitigate the most serious shortcomings of benchmark and outcome analysis.”
§ A large-scale analysis of racial disparities in police stops across the United States > Veil-of-darkness analysis.
What the sources say
- Not matchedcites Simoiu et al. 7
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Under this model of officer behaviour, we interpret lower search thresholds for one group relative to another as evidence of discrimination.”
§ A large-scale analysis of racial disparities in police stops across the United States > Veil-of-darkness analysis.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The left panel (Colorado) represents 1,674,619 stops and the right panel (Washington)represents 3,985,677 stops.”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“we applied a computationally fast variant of the threshold test developed by Pierson et al. 22 , which we fit separately on both state patrol stops and municipal police stops.”
§ A large-scale analysis of racial disparities in police stops across the United States > Veil-of-darkness analysis.
What the sources say
- Not matchedcites Pierson et al. 22
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“we re-categorized as 'Hispanic' all drivers in Texas with Hispanic names who were originally labelled 'white' or who had missing race data; this method adds about 1.9 million stops of Hispanic drivers over the period 2011-2015.”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection and standardization.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If, for example, officers disproportionately patrol black and Hispanic neighbourhoods, the downstream effects can be injurious even if individual stop decisions are not directly affected by the colour of one's skin.”
§ A large-scale analysis of racial disparities in police stops across the United States > Discussion
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“can place heavy burdens on black and Hispanic drivers without improving public safety 17 .”
§ A large-scale analysis of racial disparities in police stops across the United States > Discussion
What the sources say
- Not matchedcites [17] Chohlas-Wood, A., Goel, S., Shoemaker, A. & Shroff, R. An Analysis of the Metropolitan Nashville Police Department’s Traffic Stop Practices https://policylab.stanford.edu/media/nashville-traffic-stops.pdf (Stanford Computational Policy Lab, 2018).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“since black drivers were more likely to be charged with such offences previous to legalization, these drivers were also disproportionately impacted by the policy change.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We ran numerous automated checks to detect and correct these where possible, although some errors probably remain due to the complex nature of the data.”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection and standardization.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We did not exclude any data that were deemed sufficient to conduct our analysis.”
§ A large-scale analysis of racial disparities in police stops across the United States > Additional information
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“the KPT model of behaviour requires strong assumptions, including that drivers and officers are rational actors and that every driver has perfect knowledge of the likelihood that he or she will be searched.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
What the sources say
- Not checked — no full text of the sourcecites [28] Engel, R. S. & Tillyer, R. Searching for equilibrium: the tenuous nature of the outcome test. Justice Q. 25, 54–71 (2008). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Following previous studies 38,39 , we defined a name as 'typically' Hispanic if at least 75% of people with that name identified as Hispanic”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection and standardization.
What the sources say
- Not matchedcites [38] Word, D. & Perkins, C. Building a Spanish Surname List for the 1990s: A New Approach to an Old Problem (Population Division, US Census Bureau, 1996).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [39] Melendres v Arpaio (2009). 598 F. Supp. 2d 1025 (D. Ariz., 2009).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“For both the state patrol model and the municipal department model, the prediction errors are minimal and similar across race groups.”
§ A large-scale analysis of racial disparities in police stops across the United States > Veil-of-darkness analysis.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The threshold test, however, aims to capture only taste-based discrimination, as is common in the empirical literature on discrimination.”
§ A large-scale analysis of racial disparities in police stops across the United States > Veil-of-darkness analysis.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We did not re-categorize drivers in any other jurisdiction.”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection and standardization.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“although our results are qualitatively similar under alternative averaging schemes.”
§ A large-scale analysis of racial disparities in police stops across the United States > Veil-of-darkness analysis.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“we limit our analysis to counties and districts with at least 50 searches per race group.”
§ A large-scale analysis of racial disparities in police stops across the United States > Veil-of-darkness analysis.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Our complete data-cleaning pipeline is extensive and required subjective decisions”
§ A large-scale analysis of racial disparities in police stops across the United States > Compiling a national database of traffic stops.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The results were statistically significant and qualitatively similar in all cases.”
§ A large-scale analysis of racial disparities in police stops across the United States > Veil-of-darkness analysis.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The authors state this as an assumption.
Officers exercise discretion in whom to search.
“Implicit in this test is the assumption that officers exercise discretion in whom to search”
The authors state this as an assumption.
In the absence of discrimination, searched minority drivers should be found with contraband at the same rate as searched white drivers.
“Becker argued that even if minority drivers are more likely to carry contraband, in the absence of discrimination, searched minorities should still be found to have contraband at the same rate as searched whites.”
The authors state this as an assumption.
Officers who engage in racial profiling are less able to identify a driver's race after dark than during the day.
“Their method starts from the idea that officers who engage in racial profiling are less able to identify a driver's race after dark than during the day.”
The paper relies on this without support.
Race-specific driving behaviour is not related more to lighting than to time of day.
“Results could be skewed if race-specific driving behaviour is related more to lighting than time of day, leading the test to suggest discrimination where there is none.”
The paper relies on this without support.
Artificial lighting does not substantially weaken the relationship between sunlight and visibility.
“artificial lighting (for example, from street lamps) can weaken the relationship between sunlight and visibility, and so the method may underestimate the extent to which stops are predicated on perceived race.”
The paper relies on this without support.
Violation type is not related to lighting in a way that biases the veil-of-darkness test.
“if violation type is related to lighting, the test could give an inaccurate measure of discrimination.”
The paper relies on this without support.
Search policies do not differ within the geographic subdivisions considered.
“if search policies differ not only across, but also within, the geographic subdivisions we consider, then the threshold test might mistakenly indicate discrimination where there is none.”
The paper relies on this without support.
Officers do not disproportionately suspect more serious criminal activity when searching black and Hispanic drivers than when searching white drivers.
“if officers disproportionately suspect more serious criminal activity when searching black and Hispanic drivers compared to white drivers (for example, possession of larger quantities of contraband), then lower observed thresholds may stem from non-discriminatory police practices.”
The paper relies on this without support.
The Bayesian threshold model's functional form and prior distributions are adequate for identifying search thresholds.
“thresholds cannot be identified by the observed data alone 7 , and so inferences are dependent on the specific functional form of the underlying Bayesian model, including the prior distributions.”
The paper relies on this without support.
Marijuana legalization in Colorado and Washington was not confounded by other simultaneous changes, and parallel trends hold.
“Using a difference-in-differences strategy, we find that legalization reduced both search rates and misdemeanour rates for drug offences for white, black and Hispanic drivers-though a gap in search thresholds persists.”
The authors state this as an assumption.
The latent signal distributions for each race and county are homoskedastic discriminant distributions.
“these signal distributions are modelled as homoskedastic discriminant distributions, a class of logit-normal mixture distributions supported on the unit interval [0, 1].”
The authors state this as an assumption.
The parameters φ and δ can be decomposed into additive race and location terms.
“We impose additional structure on the signal distributions by assuming that ϕ r,d and δ r,d can be decomposed into additive race and location terms.”
The authors state this as an assumption.
For each stop, a signal is drawn from the race-and-location-specific distribution; a search occurs if the signal exceeds the race-and-location-specific threshold; if searched, contraband is found with probability equal to the signal.
“For each stop i, we draw a signal p i from the associated race-and location-specific discriminant distribution.If the signal p i exceeds the race-and location-specific threshold t ri;di I , then a search is conducted and S i = 1; otherwise there is no search and S i = 0.If a search is conducted, contraband is found with probability p i , in which case H i = 1; otherwise, H i = 0.”
The paper relies on this without support.
Recreational marijuana legalization took effect on 9 December 2012 in Washington and 10 December 2012 in Colorado.
“Measures legalizing recreational marijuana took effect on 9 December 2012 in Washington, and on 10 December 2012 in Colorado.”
The paper relies on this without support.
In Washington, probable cause searches are barred because the state does not have an automobile exception for warrantless searches.
“in Washington, we note that probable cause searches are barred because the state does not have an automobile exception for warrantless searches.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“These disparities exist even though they cannot be explained by the belief that Black people supposedly commit more crimes, including violent crimes”
From A call to dismantle systemic racism in criminal legal systems. (2022) · cites this paper as Pierson et al., 2020
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Another study, which examined one hundred million traffic stops across the United States, also found that police were less likely to discover contraband or evidence when searching Black and Hispanic drivers than White drivers.”
From The Rise of the Carceral State: Foundations and Contours of a Rapidly Changing Criminal Legal System (2025) · cites this paper as Pierson et al. 2020
The cited source says what the claim attributes to it.
In the citing paper
“They also found that Black drivers were less likely to be stopped by police after sunset-or, put another way, more likely to be stopped during daytime hours when skin color is more readily apparent (Pierson et al. 2020).”
From The Rise of the Carceral State: Foundations and Contours of a Rapidly Changing Criminal Legal System (2025) · cites this paper as Pierson et al. 2020
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Skin tone (i.e., colorism) has long been identified as among the most salient markers for discrimination (10, 11).”
From Nobel and novice: Author prominence affects peer review (2022) · cites this paper as [10]
Experimental — accuracy not yet measuredIn the citing paper
“SROs do not effectively reduce crime in schools (60)”
From Structural Racism and Suicide Prevention for Ethnoracially Minoritized Youth: A Conceptual Framework and Illustration Across Systems (2022) · cites this paper as [60]
Citations
Cites
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
Mostly Harmless Econometrics: An Empiricist's Companion
Angrist, J. D., & Pischke, J.-S. (2009) · doi:10.1515/9781400829828 · Cited by 9,467
Mostly Harmless Econometrics: An Empiricist's Companion
Angrist, J. D., & Pischke, J.-S. (2008) · doi:10.2307/j.ctvcm4j72 · Cited by 5,051
The Economics of Discrimination.
Collard, D. A., & Becker, G. S. (1972) · The Economic Journal · doi:10.2307/2230052 · Cited by 3,642
The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte CarloPreprint
Homan, M. D., & Gelman, A. (2014) · arXiv (Cornell University) · Cited by 3,078
POSTERIOR PREDICTIVE ASSESSMENT OF MODEL FITNESS VIA REALIZED DISCREPANCIES
Gelman, A., Meng, X., & Stern, H. S. (1996) · Cited by 1,982
Gardner, W. P., Mulvey, E. P., & Shaw, E. C. (1995) · Psychological Bulletin · doi:10.1037/0033-2909.118.3.392 · Cited by 1,840
The Economics of Discrimination
Becker, G. S. (1971) · doi:10.7208/chicago/9780226041049.001.0001 · Cited by 1,794
Nobel Lecture: The Economic Way of Looking at Behavior
Becker, G. S. (1993) · Journal of Political Economy · doi:10.1086/261880 · Cited by 1,619
Research works printed but not linked: 4
- Pierson, E., Corbett-Davies, S. & Goel, S. In International Conference on Artificial Intelligence and Statistics (eds Storkey, A. & Fernando Perez-Cruz, F.) 96–105 (PMLR, 2018).
- Jung, J., Concannon, C., Shroff, R., Goel, S. & Goldstein, D.G. Simple rules to guide expert classifications. J. R. Stat. Soc. Ser. A (in the press).
- Arrow, K. in Discrimination in Labor Markets (eds Ashenfelter, O. & Rees, A.) 3–33 (Princeton Univ. Press, 1973).
- Phelps, E. The statistical theory of racism and sexism. Am. Econ. Rev. 62, 659–661 (1972).
Cited by
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Items 301–400 of 548
Data-driven policing: how digital technologies transform the practice and governance of policing
Marciniak, D. (2021) · Open Access at Essex (University of Essex) · Cited by 2
An Algorithmic Assessment of Parole DecisionsPreprint
Laqueur, H. S., & Copus, R. W. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4078152 · Cited by 2
Exposure to Discretionary Arrests Increases Support for Anti-Police ProtestsPreprint
Chen, T. H. Y., McLachlan, P., & Fariss, C. J. (2021) · doi:10.31235/osf.io/r78ys · Cited by 2
It's About Time: Examining Inequality in the Time Cost of WaitingPreprint
Holt, S., & Vinopal, K. M. (2021) · doi:10.31235/osf.io/jbk3x · Cited by 2
The Rise of the Carceral State: Foundations and Contours of a Rapidly Changing Criminal Legal System
Wakefield, S., & Turney, K. (2025) · RSF: The Russell Sage Foundation Journal of the Social Sciences · doi:10.7758/rsf.2025.11.3.04 · Cited by 3
“They also found that Black drivers were less likely to be stopped by police after sunset—or, put another way, more likely to be stopped during daytime hours when skin color is more readily apparent (Pierson et al. 2020).”
Jones, T. V. (2021) · Open Journal of Social Sciences · doi:10.4236/jss.2021.95013 · Cited by 1
Brown, M. (2021) · Advances in human resources management and organizational development book series · doi:10.4018/978-1-7998-7016-6.ch007 · Cited by 1
Jackson, S. M., & Rippy, M. (2022) · American Journal of Criminal Justice · doi:10.1007/s12103-022-09692-7 · Cited by 1
“Traffic stops are the most visible branch of law enforcement activities and generate the bulk of interactions with the community (BJS, 2018; Fliss et al., 2020; Pierson et al., 2020).”
Making Drug use Dangerous for Black Men: Race, Drugs, Violence, and Criminal Justice
Seffrin, P. M., & Teeple, J. (2022) · Race and Justice · doi:10.1177/21533687221127446 · Cited by 1
“Relatedly, the “ hit rate ” in vehicle searches for illegal drugs have been shown to be slightly higher for White motorists than for Black motorists, even though Black motorists are more likely to be searched (Pierson, et al., 2020).”
Shoub, K. (2024) · The Journal of Politics · doi:10.1086/730727 · Cited by 1
Gentrification and Racial Distrust in Communities: Evidence from 911 Calls
Ananthakrishnan, U. M., Hasan, S., & Kumar, A. (2022) · Management Science · doi:10.1287/mnsc.2022.00362 · Cited by 2
Vogel, M., Ivanov, S., & Williams, J. H. (2024) · Race and Social Problems · doi:10.1007/s12552-024-09425-x
“Relative to Whites, Black and Hispanic Americans are more likely to be stopped and arrested (Crutchfield et al., 2012; Gelman et al., 2007; Pierson et al., 2020).”
Eliminating racial disparities in policing: a social work infusion model
Palacio, H. H., & Hays, K. (2025) · Journal of Ethnic & Cultural Diversity in Social Work · doi:10.1080/15313204.2025.2465537 · Cited by 1
“This data would be used to identify demographic areas where strategies are needed to help oppressed and marginalized groups (Pierson et al., 2020).”
Embedding Implicit Bias Education into Curricular Interventions: Insights for Criminology Programs
Smith, A. N., & Porter, C. N. (2025) · Journal of Criminal Justice Education · doi:10.1080/10511253.2025.2472424 · Cited by 1
A Primer on Privileged Omissions: Effects and Dimensions of Influence
Conyers, A., & Carrizales, T. J. (2025) · Perspectives on Public Management and Governance · doi:10.1093/ppmgov/gvaf010 · Cited by 1
Onakomaiya, D. O., Bubu, O. M., & Schoenthaler, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-23750-8.00014-x · Cited by 1
Böing, B., de Vries, P. W., & Giebels, E. (2025) · Frontiers in Virtual Reality · doi:10.3389/frvir.2025.1451206 · Cited by 2
Racial Profiling in Traffic Stops: An Investigation of Race/Ethnicity and Minor Traffic Violations
Iwama, J., & McDevitt, J. (2025) · Race and Justice · doi:10.1177/21533687251346502 · Cited by 1
“Our fi ndings support the argument that persons of color are more likely to be searched during a traf fi c stop as shown in previous studies (Baumgartner et al., 2016; Pierson et al., 2020).”
Anumba, N. M. (2025) · Springer eBooks · doi:10.1007/978-3-031-90279-6_4 · Cited by 1
Chang, A. (2025) · Public Administration Review · doi:10.1111/puar.70010 · Cited by 1
“First, it uses FHP's data from the Stanford Open Policing Project, which provides each officer's ID, detailed demographics of officers and drivers, reasons for stops, and whether citations were issued (Pierson et al. 2020”
State-by-State Review: The Spread of Law Enforcement Accountability Policies
Zare, H., Gilmore, D. R., Balsara, K., Pargas, C., Valek, R. A., Ponce, A. N., Masoudi, N., Spencer, M., Warren, T., & Crifasi, C. K. (2025) · Social Sciences · doi:10.3390/socsci14080483 · Cited by 4
Skin color stratification in police contact among Asian-descent people
Baluran, D. A. (2025) · Social Problems · doi:10.1093/socpro/spaf033 · Cited by 2
Ebert, J. P., McDonald, C. C., Xiong, R. A., Abdel-Rahman, D., Khan, N., Irving, M., Lee, A. Y. H., Patel, A., Friday, S., Tefft, B. C., et al. (2025) · Accident Analysis & Prevention · doi:10.1016/j.aap.2025.108331 · Cited by 2
Maturino, K., Chang, Y., Mahoney, L., Johnson, N., Teixeira, C., & Lee, Y. (2025) · Addiction Research & Theory · doi:10.1080/16066359.2025.2605951 · Cited by 1
“Black and Hispanic drivers are still more likely to be stopped and searched by police compared to White drivers (Pierson, 2020).”
Ellyson, A. M., Kafka, J., Adhia, A., et al. (2026) · Journal of Criminal Justice · doi:10.1016/j.jcrimjus.2025.102591 · Cited by 1
Machines Do See Color: Using LLMs to Classify Overt and Covert Racism in Text
Gordillo, D. D., Timoneda, J. C., & Vera, S. V. (2026) · Sociological Methods & Research · doi:10.1177/00491241251412360 · Cited by 1
Lo, A., Renshon, J., & Bassan‐Nygate, L. (2026) · Nature Human Behaviour · doi:10.1038/s41562-026-02413-8 · Cited by 2
“…outgroup bias carries pernicious consequences across economic (job discrimination), health (for example, odds of long-standing illnesses and maternal mortality) and judicial/policing (bail decisions, traffic stops) domains, and overall lower pro-social behaviours towards outgroup members 3–9 .”
Jurkus, M., Castañeda, J., Strıke, C., Law, J., Jayathilake, A., Perlman, C. M., & Bardwell, G. (2026) · International Journal of Drug Policy · doi:10.1016/j.drugpo.2026.105249 · Cited by 2
Race and Empathy for the Police
Graham, A., & Cullen, F. T. (2026) · Criminal Justice and Behavior · doi:10.1177/00938548261426468 · Cited by 3
“…Black Americans experience disproportionate uses of force by police (Morrow et al., 2017; Paoline et al., 2018), are stopped and searched by police (Pierson et al., 2020), and are arrested at higher rates than White Americans for illicit substances (Rouhani et al., 2024), despite similar use…”
A Qualitative Study of the Impact of Police Violence on Black Birthing People
Smart, B. P., Butler, M. S., Freedman, A. A., Fernando, T., Cordero, J., Premkumar, A., & Keenan‐Devlin, L. S. (2026) · Journal of Racial and Ethnic Health Disparities · doi:10.1007/s40615-026-02964-6 · Cited by 1
Jauk, D., Peralta, R. L., Jasper-Morris, A., Payne, L., Xi, J., & Luo, X. I. (2026) · Feminist Criminology · doi:10.1177/15570851261443539 · Cited by 1
“They used it to speak of their own experiences shaped by systemic racism and historical state-sponsored oppression of Black communities (Alexander, 2010; Hinton, 2021; Muhammad, 2010; Pierson et al., 2020).”
Police experiences with cannabis legalization and perceptions of broader drug policy reform
Silberzahn, B. E., Schneider, K. E., Sisson, L. N., Winiker, A. K., Weiss, M., del Pozo, B., & Rouhani, S. (2026) · International Journal of Drug Policy · doi:10.1016/j.drugpo.2026.105356 · Cited by 1
Kahsay, E., Sneed, R. S., Zhong, C., Kalesnikava, V. A., & Mezuk, B. R. (2026) · Sociological Focus · doi:10.1080/00380237.2026.2627642 · Cited by 1
de Baca, T. C., & Menie, M. A. W. O. (2018) · Journal of Criminal Justice · doi:10.1016/j.jcrimjus.2018.05.009 · Cited by 1
Ethnic Identity of Black Muslims and Resilience from Criminal Risk Factors
Simmons‐Horton, S. Y., & Gibson, C. A. (2021) · Humanity & Society · doi:10.1177/01605976211001554 · Cited by 1
“…disparities in police interactions from data of over 60 million state patrol stops, across 20 states in America, finding that Blacks drivers experienced more stops compared to Whites and Hispanics, and Blacks were more likely than Whites to be ticketed, searched, and arrested (Pierson et al. 2017).”
BackgroundLaurenceau, I., Jones, J. L., Moon, D., et al. (2020) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas50296.2020.9462228 · Cited by 1
Hajnal, Z. L., Hutchings, V. L., & Lee, T. (2024) · Cambridge University Press eBooks · doi:10.1017/9781108633352.018 · Cited by 1
Race, excess suspicion, and larceny in Upstate NY
Massey, S. G., Kauffman, R. A., Chen, M., & Tu, W. (2022) · Criminal Justice Studies · doi:10.1080/1478601x.2022.2081966 · Cited by 1
“It has been identified in a variety of contexts, such as traffic stops (Pierson et al., 2020; Lundman, 2010; D. A. Harris, 2002), stop-and-frisk practices and criminal investigations (Fradella, Morrow, & White, 2021; Fradella & White, 2017; Gelman, Fagan, & Kiss, 2007; Stoudt, Fine, & Fox, 2011),…”
Insights from Social Psychology: Racial Norms, Stereotypes, and Discrimination
Seguino, S. (2022) · Springer eBooks · doi:10.1007/978-981-33-4016-9_47-1 · Cited by 1
Brewer, B. (2023) · The B E Journal of Economic Analysis & Policy · doi:10.1515/bejeap-2022-0308 · Cited by 1
Young, J. L., Farinde‐Wu, A., & Sibanda, L. (2023) · Studying Teacher Education · doi:10.1080/17425964.2023.2240327 · Cited by 4
“Credence to the existence of the ‘while Black’ phenomenon is growing as numerous empirical studies have validated its existence as new instances continue to emerge (Pierson et al., 2020).”
BackgroundPrivileged Omissions: The Impact of Discriminatory Inaction
Conyers, A., & Carrizales, T. J. (2023) · Administration & Society · doi:10.1177/00953997231204980 · Cited by 1
“For instance, the “veil of darkness” hypothesis proposes that law enforcement is less likely to racially profile traffic stops when it is dark (Grogger & Ridgeway, 2006; Pierson et al., 2020).”
BackgroundGasnick, A., Sarode, A. L., Badrinathan, A., et al. (2023) · Injury · doi:10.1016/j.injury.2023.111241 · Cited by 1
Racism and violence in policing: Perspectives from a juvenile prison
Fix, R. L. (2024) · Journal of Community Psychology · doi:10.1002/jcop.23104 · Cited by 1
Iwama, J. (2024) · Policing An International Journal · doi:10.1108/pijpsm-12-2023-0167 · Cited by 1
Methodological Challenges for Research on Racial Bias in Police Shootings
Murphy, J. P., Neil, R., & Paige, J. W. (2024) · RAND Corporation eBooks · doi:10.7249/rra243-8 · Cited by 1
Intersectionality of Racial Profiling: A Call for a Broader Understanding
Wilopo, C. S., & Plümecke, T. (2024) · Springer eBooks · doi:10.1007/978-3-031-52227-7_22 · Cited by 1
Thorstad, D. (2024) · Oxford University Press eBooks · doi:10.1093/oso/9780198886143.003.0003 · Cited by 2
The Heat is On: Does Civil Litigation Affect Policing Practices?
Bird, C. C., & Shannon, B. N. (2024) · Political Research Quarterly · doi:10.1177/10659129241266824 · Cited by 1
Reparations of the horse? Algorithmic reparation and overspecialized remedies
Doyle, C., Alvarez-Garcia, M., Tracey, P., Grill, G., Whitney, C. D., & Chambers, L. M. (2024) · Big Data & Society · doi:10.1177/20539517241270670 · Cited by 1
“Across the country, police disproportionately enforce traf fi c offenses against minority communities without algorithmic assistance (Pierson et al., 2020).”
Addressing discretization-induced bias in demographic prediction
Dong, E., Schein, A., Wang, Z., & Garg, N. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf027 · Cited by 2
“…such as in lending and policing [Andrus et al., 2021, Chen et al., 2019, Consumer Financial Protection Bureau, 2014, Edwards et al., 2019, Pierson et al., 2020, Zhang, 2018], making more fair and accurate decisions in voterfile-based polling and turnout campaigns [Fraga, 2016a,b, 2018,…”
BackgroundInfluentialThe nature of discrimination in recreation decision making
Backstrom, J. D., & Woodward, R. T. (2025) · American Journal of Agricultural Economics · doi:10.1111/ajae.12545 · Cited by 2
Race-gender Disparities in the Criminalization and Medicalization of Mental Illness
Rainock, M. (2025) · Society and Mental Health · doi:10.1177/21568693251335336 · Cited by 1
Police-related Stress and Carotid Intima Media Thickness Among African American Women
Hoggard, L. S., Murden, R., Fields, N. D., Erving, C. L., Udaipuria, S., Moore, R. H., Vaccarino, V., Quyyumi, A. A., Barinas‐Mitchell, E., & Lewis, T. T. (2025) · Psychosomatic Medicine · doi:10.1097/psy.0000000000001428 · Cited by 1
Camp, N. P., Prabhakaran, V., Hetey, R. C., Monin, B., Jurafsky, D., & Eberhardt, J. L. (2025) · Journal of Social Issues · doi:10.1111/josi.70017 · Cited by 4
Using change in a seat belt law to study racially-biased policing in South Carolina
Riddell, C. A., Kaufman, J. S., Torres, J. M., & Harper, S. B. (2019) · Preventive Medicine · doi:10.1016/j.ypmed.2019.105884 · Cited by 1
It Don’t Take Much to Piss Off a Cop
Rhodes, W. A. (2020) · Delaware Journal of Public Health · doi:10.32481/djph.2020.11.011 · Cited by 1
Schnitzer, K., Cather, C., Potter, K. W., Kaba, H., Marsden, J., Hoffman, D. R., & Shtasel, D. L. (2021) · Journal of Health Care for the Poor and Underserved · doi:10.1353/hpu.2021.0021 · Cited by 1
Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA
Tufail, M., & Gul, S. (2022) · KIET Journal of Computing and Information Sciences · doi:10.51153/kjcis.v5i2.135 · Cited by 1
“More than 20 million Americans are stopped each year for traffic violations [34].”
BackgroundStereotypes and the Administration of Justice
O’Flaherty, B., & Sethi, R. (2022) · Springer eBooks · doi:10.1007/978-981-33-4016-9_10-1 · Cited by 1
Stereotypes and the Administration of Justice
O’Flaherty, B., & Sethi, R. (2023) · Springer eBooks · doi:10.1007/978-981-19-4166-5_10 · Cited by 1
The nature, detection, and avoidance of harmful discrimination in criminal justice
O’Flaherty, B., Sethi, R., & Williams, M. C. (2023) · Journal of Policy Analysis and Management · doi:10.1002/pam.22527 · Cited by 1
Racial Disparities in Policing
Burke, K. C., Najdowski, C. J., & Stevenson, M. C. (2023) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197549513.013.26 · Cited by 1
Family income trajectories and early child development: a latent class growth analysis
Riser, Q. H. (2020) · Iowa State University Digital Repository (Iowa State University) · doi:10.31274/etd-20210114-126 · Cited by 1
Stereotypes and the Administration of JusticePreprint
O’Flaherty, B., & Sethi, R. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3808745 · Cited by 1
It's About Time: Examining Inequality in the Time Cost of WaitingPreprint
Holt, S., & Vinopal, K. M. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3857883 · Cited by 1
Mitchell, J., & Chihaya, G. K. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4001792 · Cited by 1
“I feel Threatened”: Measuring Racial Distrust in America from 911 CallsPreprint
Ananthakrishnan, U. M., Hasan, S., & Kumar, A. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4014937 · Cited by 1
The Roots of Racialized Travel BehaviorPreprint
Barajas, J. M. (2021) · doi:10.31235/osf.io/unmkx · Cited by 1
Policing the Police: Examining the Role of News Reports on Racially-Biased PolicingPreprint
Ananthakrishnan, U. M., Chan, J., & Song, Y. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4152235 · Cited by 1
Wang, X. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4158042 · Cited by 1
Strategic Bureaucratic Opacity: Evidence from Death Investigation Laws and Police KillingsPreprint
Celislami, E., Kastoryano, S., & Mastrobuoni, G. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4636865 · Cited by 1
The biased enforcement of rarely followed rulesPreprint
Wylie, J., Milless, K. L., Sciarappo, J., & Gantman, A. P. (2024) · doi:10.31234/osf.io/jzamn · Cited by 1
Pryor, M., Heydari, F., Goff, P. A., & Friedman, B. D. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4798142 · Cited by 1
Strategic Bureaucratic Opacity: Evidence from Death Investigation Laws and Police KillingsPreprint
Celislami, E., Kastoryano, S., & Mastrobuoni, G. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4800790 · Cited by 1
A non-expert’s introduction to data ethics for mathematicians
Porter, M. A. (2025) · Proceedings of symposia in applied mathematics · doi:10.1090/psapm/080/00728 · Cited by 3
Clustering and Predicting Driving Violations Using Web-Enabled Big Data Techniques
Prayaga, L., Devulapalli, K., Devulapalli, S., et al. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-0617-4_49
Discussion of Fairness and Implementability in Stackelberg Security Games
Víctor Bucarey, L., & Labbé, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32430-8_7
“For instance, according to [10], officers generally stop black drivers at higher rates than white drivers, and stop Hispanic drivers at similar or lower rates than white drivers.”
BackgroundKissinger, A. (2020) · eScholarship (California Digital Library)
Crozier, W. E., Garrett, B. L., & Modjadidi, K. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3645308
Russell, J. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.04342
“Another found that Black people are 83 percent more likely to be arrested for marijuana compared to White people, despite similar rates of marijuana usage across racial groups [5].”
BackgroundMajor physics society won’t meet in cities with racist policing record
Subbaraman, N. (2021) · Nature · doi:10.1038/d41586-021-00492-3
Nwanaji‐Enwerem, J. C., Nwanaji-Enwerem, O., & Johnson, K. R. (2021) · Journal of Transport & Health · doi:10.1016/j.jth.2021.101029
Analyzing Societal Bias of California Police Stops Through Lens of Data Science
Manna, S., & Bunyard, S. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-80126-7_9
An Analysis and Visualization of Best Practices for Police Data Transparency
Arango, M. C., Hogue, A., & Williams, K. (2021) · Systems and Information Engineering Design Symposium · doi:10.1109/sieds52267.2021.9483786
Mitchell, J., & Chihaya, G. K. (2021) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/fu8rg
Racial profiling and Section 163 traffic stops: is there cause for concern?
Adams-Guppy, J. R., & Simpson, P. (2022) · Safer Communities · doi:10.1108/sc-08-2021-0029
A Socio-Behavioral Model of Racism Against the Black Community and Avenues for Anti-Racism ResearchPreprint
Belisle, J., Payne, A. N., & Paliliunas, D. (2022) · doi:10.31234/osf.io/akq6h
On Mentoring: Multigenerational Reflections with James E. Blackwell
Dixon-Reeves, R. (2019) · Issues in Race and Society An Interdisciplinary Global Journal--Special Blackwell Edition · doi:10.34314/issuescomplete2019.00001
Ryan, M. E. (2021) · Springer eBooks · doi:10.1007/978-1-4614-7883-6_685-2
Racial Distance Matters Too: Home Bias and Racial Influence in Recreation Decision MakingPreprint
Backstrom, J. D., & Woodward, R. T. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3818265
Anti-Oppressive Restorative Justice: Behavior Analysis in Alternatives to PolicingPreprint
Leland, W., & Stockwell, A. (2021) · doi:10.31234/osf.io/wq9g2
Identifying and Addressing Structural Racism
Yang, C., Alsan, M. M., & Jones, D. (2021) · AEA Randomized Controlled Trials · doi:10.1257/rct.7246 · Cited by 1
9. Enforcement, Monitoring, and Surveillance
(2021) · American Public Health Association eBooks · doi:10.2105/9780875533186ch09
Missing “Get Tough” Risk Factors
(2019) · Advances in public policy and administration (APPA) book series · doi:10.4018/978-1-7998-1147-3.ch010
A Transparency Statement Transforms Community-Police InteractionsPreprint
Yeager, D. S., Dobson, K. S. H., & Dittmann, A. G. (2022) · Research Square · doi:10.21203/rs.3.rs-1726104/v1
Measuring everyday experiences with the police: An experience sampling pilot studyPreprint
Nivette, A. E., de Weegh, A. O., & Nägel, C. (2022) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/wc846
Bolas, T. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4094498
Social Justice Implications for Black Men’s Health: Policing Black Bodies
Robinson, M. A. (2022) · Springer eBooks · doi:10.1007/978-3-031-04994-1_13
Metadata
- Authors
- Pierson, E., Simoiu, C., Overgoor, J., Corbett-Davies, S., Jenson, D., Shoemaker, A., Ramachandran, V., Barghouty, P., Phillips, C., Shroff, R., & Goel, S.
- Affiliations
- Stanford University (US)
- New York University (US)
- Corresponding author
- Goel, Sharad
- Venue
- Nature Human Behaviour
- Published
- 2020-05-04
- Volume
- 4
- Issue
- 7
- Pages
- 736-745
- ISSN
- 2397-3374 (ISSN-L)
- OpenAlex
- W2705765409
- Cited by
- 552
- References
- 42
- Citations by year
- 2026: 59 · 2025: 74 · 2024: 97 · 2023: 109 · 2022: 79 · 2021: 71 · 2020: 34
- FWCI
- 254.50
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Human Behaviour (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University)
- Funders
- John S. and James L. Knight Foundation
- Hellman Foundation
- Topics
- Social Sciences › Political Science and International Relations › Policing Practices and Perceptions
- Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
- Social Sciences › Education › Education Discipline and Inequality
- Keywords
- racial disparities in policing, traffic stops, police searches, racial bias, Black drivers, recreational cannabis legalization, contraband, racial profiling, United States
- MeSH terms
- Automobile Driving, Black or African American, Female, Hispanic or Latino, Humans, Male, Time Factors, United States, Police, White People, Racism
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.
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