Enhancing Diversity, Skills Development, and Interest in STEM Education through Ontario Tech's Engineering Outreach Programs
Bibliographic record
Abstract
Ontario Tech University’s Engineering Outreach programs annually involve over 30,000 young individuals in STEM workshops and events, targeting young women and other groups historically underrepresented in STEM to encourage their pursuit of Engineering careers. This paper examines the effectiveness of these efforts and evaluates our outreach programs' impact on increasing participation and opportunities for youth underrepresented in STEM fields. While long-term outcomes like career influence are challenging to measure, our initial findings indicate a heightened interest and improved access for underrepresented groups in STEM education. These early outcomes suggest a considerable shift in perceptions regarding Engineering as an appealing and viable career path for underserved populations, reinforcing our belief in the substantial positive influence of our outreach programming and initiatives. The culmination of these findings provides compelling evidence to support the notion that Engineering Outreach effectively shapes positive perceptions and fosters interest in STEM education, particularly among demographics traditionally underrepresented in STEM fields.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".