THE EFFECT OF OUTREACH PROGRAMS ON INCREASING FEMALE ENROLLMENT IN ENGINEERING
Bibliographic record
Abstract
The percentage of female undergraduate applicants and first-year student in engineering is increasing in the Faculty of Applied Science and Engineering (FASE) at the University of Toronto (UofT). Outreach programs are used to encourage high school students by gaining exposure and knowledge regarding the field of engineering. The effectiveness of these outreach programs in mitigating academic and social barriers is a key point of interest examined in the paper, specifically those catered directly to female students. This research analyzes the growing number of community outreach programs offered at the University of Toronto. We examined the effect of three outreach initiatives: the DaVinci Engineering Enrichment Program (DEEP), the Girls Leadership in Engineering Experience (GLEE), and the Young Women in Engineering Symposium (YWIES). Using statistical data from the FASE outreach office and participation feedback from the events, we compared the enrollment statistics, the percentage of students who chose engineering, and what students found most useful in events. Observations prove that although the events encourage the same number of female students entering engineering, however, suggest that eliminating social barriers and stereotypes influence the increasing number of female-enrollment.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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".