A Faculty Initiative Addressing Gender Disparity at a Small STEM-Focused University: A Case Study
Bibliographic record
Abstract
The gender gap and ethnic diversity are historic challenges in computer science (CS) that have faced a lack of progress in the past half-decade. Four CS faculty members explored and investigated the issue of gender gap at a small, newly established, STEM-focused institution. This institution is dedicated to primarily undergraduate teaching and serving many first-generation university students. We collected statistics about women studying CS and the obstacles they face as they enter CS programs. To collect best practices for improving equity, diversity, and inclusion, we attended CS education conferences and discussed within a focus group at the institution. We then implemented the set of initiatives found. There were several challenges in the process: limited participation from faculty members in our focus group, barriers in conducting a survey at a large conference, and difficulty in engaging the faculty and disseminating knowledge. We summarize crucial insights gained from our efforts in this initiative, which could be valuable for future implementation of similar initiatives in other small, newly established post-secondary institutions.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".