The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
GenderMag: A Method for Evaluating Software's Gender Inclusiveness
Abstract
In recent years, research into gender differences has established that individual differences in how people problem-solve often cluster by gender. Research also shows that these differences have direct implications for software that aims to support users' problem-solving activities, and that much of this software is more supportive of problem-solving processes favored (statistically) more by males than by females. However, there is almost no work considering how software practitioners—such as User Experience (UX) professionals or software developers—can find gender-inclusiveness issues like these in their software. To address this gap, we devised the GenderMag method for evaluating problem-solving software from a gender-inclusiveness perspective. The method includes a set of faceted personas that bring five facets of gender difference research to life, and embeds use of the personas into a concrete process through a gender-specialized Cognitive Walkthrough. Our empirical results show that a variety of practitioners who design software—without needing any background in gender research—were able to use the GenderMag method to find gender-inclusiveness issues in problem-solving software. Our results also show that the issues the practitioners found were real and fixable. This work is the first systematic method to find gender-inclusiveness issues in software, so that practitioners can design and produce problem-solving software that is more usable by everyone.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
In the citing paper
“Software developers, like all humans, are cognitively diverse (e.g., they differ in attitudes toward risk, information-processing and learning preferences, motivations for using a technology), which impacts how they problem-solve, manage uncertainty in work, or even engage with genAI [7, 19, 79, 83, 101, 126].”
From What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI (2025) · cites this paper as [19]
In the citing paper
“When tools misalign with users' cognitive styles [6, 19, 142], they impose additional usability barriers, forcing users to expend extra cognitive effort to achieve the same outcomes.”
From What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI (2025) · cites this paper as [19]
In the citing paper
“Learning style for technology (by process vs. by tinkering) reflects how an individual approaches learning new technologies [19].”
From What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI (2025) · cites this paper as [19]
In the citing paper
“Information processing style influences how individuals interact with technology when problem-solving: some gather information comprehensively to develop a detailed plan before acting; others gather information selectively , acting on initial promising cues and acquiring more as needed [19].”
From What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI (2025) · cites this paper as [19]
In the citing paper
“Risk-tolerant individuals (one end of the spectrum) experiment more with unproven technology than risk-averse ones (the other end) [19], and report stronger intentions to use new tools [139].”
From What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI (2025) · cites this paper as [19]
4 more claims about this paper
Create a free account to see every claim other papers make about this one.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
WEST, C., & ZIMMERMAN, D. H. (1987) · Gender & Society · doi:10.1177/0891243287001002002 · Cited by 8,954
Cross-national patterns of gender differences in mathematics: A meta-analysis.
Else-Quest, N. M., Hyde, J. S., & Linn, M. C. (2010) · Psychological Bulletin · doi:10.1037/a0018053 · Cited by 1,630
Revisiting gender differences: what we know and what lies ahead.
Meyers‐Levy, J., & Loken, B. (2015) · Journal of Consumer Psychology · doi:10.1016/j.jcps.2014.06.003 · Cited by 614 · cited 2×
“In essence, when problem-solving, females are more statistically likely to use comprehensive information processing styles—gathering fairly complete information before proceeding—whereas males are more statistically likely to use selective styles— following the first promising information, then potentially backtrackin…”
Guidelines are only half of the story: accessibility problems encountered by blind users on the web
Power, C., Freire, A., Petrie, H., & Swallow, D. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2207736 · Cited by 327
How do designers and user experience professionals actually perceive and use personas?
Matthews, T., Judge, T., & Whittaker, S. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2208573 · Cited by 245
Personas is applicable: a study on the use of personas in Denmark
Nielsen, L., & Storgaard Hansen, K. (2014) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557080 · Cited by 140
End-user debugging strategies: A sensemaking perspective
Grigoreanu, V., Burnett, M., Wiedenbeck, S., Cao, J., Rector, K., & Kwan, I. (2012) · ACM Transactions on Computer-Human Interaction · doi:10.1145/2147783.2147788 · Cited by 60
Self-citation
Cited by
Guidelines for Human-AI Interaction
Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., et al. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300233 · Cited by 2,439
Using an LLM to Help With Code Understanding
Nam, D., Macvean, A., Hellendoorn, V., Vasilescu, B., & Myers, B. (2024) · Proceedings of the IEEE/ACM 46th International Conference on Software Engineering · doi:10.1145/3597503.3639187 · Cited by 487
“%) was statistically significantly lower ( 𝜒 2 (1, 66), 𝑝 < .”
BackgroundToday was a good day: The daily life of software developers
Meyer, A. N., Barr, E. T., Bird, C., & Zimmermann, T. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2904957 · Cited by 123
Open source barriers to entry, revisited: a sociotechnical perspective
Mendez, C., Padala, H. S., Steine-Hanson, Z., Hilderbrand, C., Horvath, A., Hill, C., Simpson, L., Patil, N., Sarma, A., & Burnett, M. (2018) · Proceedings of the 40th International Conference on Software Engineering · doi:10.1145/3180155.3180241 · Cited by 118
Self-citationFeine, J., Gnewuch, U., Morana, S., & Maedche, A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-39540-7_6 · Cited by 99
Metadata
- Authors
- Burnett, M., Stumpf, S., Macbeth, J., Makri, S., Beckwith, L., Kwan, I., Peters, A., & Jernigan, W.
- Venue
- Interacting with Computers
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-01-27
- Volume
- 28
- Issue
- 6
- Pages
- 760-787
- ISSN
- 0953-5438 (ISSN-L)
- 1873-7951
- OpenAlex
- W2338034815
- Cited by
- 293
- References
- 7
- FWCI
- 25.01
- Type
- journal article
- Language
- en
- Open access
- closed
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.
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: GenderMag: A Method for Evaluating Software's Gender Inclusiveness. https://app.cochat.ai/papers/10.1093/iwc/iwv046
- The paper
- Burnett, M., Stumpf, S., Macbeth, J., Makri, S., Beckwith, L., Kwan, I., Peters, A., & Jernigan, W. (2016). GenderMag: A Method for Evaluating Software's Gender Inclusiveness. Interacting with Computers. https://doi.org/10.1093/iwc/iwv046
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
