How CS departments are managing the enrolment boom: Troubling implications for diversity
Bibliographic record
Abstract
Enrolments in North American undergraduate computer science have been booming in recent years, and many CS departments have been struggling to meet student demand. We surveyed 78 CS professors, instructors, staff, and administrators to see how the enrolment boom has been affecting their practice; and to see how departments are responding in terms of policy. We asked participants to tell us what factors were being considered in their department's policymaking using a page of open-ended questions. Only one participant of 78 noted diversity as a concern. We then gave them a list of factors we thought could affect their department's policymaking, including diversity. After this prompt, more participants reported diversity was important (n=5). We found that policymakers are favouring solutions which are intuitive to them, rather than looking for examples from the literature, similar institutions, or the history of their own institution. Problematically, many of these favoured approaches have historically been linked to having a negative impact on demographic diversity in CS programmes. This could exacerbate the low participation of underrepresented groups in computer science, and undermine efforts to improve diversity.
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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 | 0.040 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.038 | 0.016 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".