TOWARDS EQUITABLE REPRESENTATION OF WOMEN IN ENGINEERING AT A TECHNICAL INSTITUTION: A CASE STUDY
Bibliographic record
Abstract
In North America, women account for a lowpercentage of the student population in technology-related fields such as computer science and engineering. While rates of women enrolled in these programs have risen since 1980, the fields remain male-dominated. Why are women so underrepresented in engineering?Technical institutions, such as MIT, have been able toachieve near gender balance in some of their programs.While this was a goal MIT strived for over the past 15years, it shows that common reasons for reducedenrollment rates of women may be addressed by theinstitutions themselves.As a polytechnic institution offering trades, diploma,and accredited degree programs, the institution described in this case serves students from a wide variety of backgrounds with a wide variety of career goals. But the number of women in these programs remains low. We hope that by implementing programs and methods proven toimprove enrollment rates of women at other institutionsBCIT will be able to increase the number of women in theirengineering programs.This paper includes an overview of the institute, anoutline of some of the broader context of engineeringeducation in North America, a description of anintervention carried out by the authors, as well aspreliminary survey and discussion results from theintervention. This paper offers first steps for others whomay want to facilitate discussion at their 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".