How Much Do You Know About Your Users? A Study of Developer Awareness About Diverse Users
Bibliographic record
Abstract
In our increasingly diverse digital landscape, under-standing and accommodating the needs of various user groups is crucial. Our research paper investigates the understanding and practices that developers have of considering diversity dimensions within their user base, in terms of Race and Ethnicity, Gender, Disability, Neurodiversity, and Age. In this research preview, we report on a preliminary mixed-method study that used an online questionnaire and interviews to collect input on developers' perceptions and measures for considering user diversity and inclusion (D&I) in the products they develop. Our findings indicate that developers from some underrepresented groups tend to exhibit greater awareness of user diversity, and their membership might have a positive effect on their team and company's perception of D&I. Our study highlights the need to enhance developer empathy and broaden their awareness about diversity. This research highlights the pivotal role of developers in creating inclusive software and underscores the importance of integrating diversity and inclusion principles into software development processes for a more representative and inclusive digital environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".