Empowering diversity in science, technology, engineering, and mathematics through university-led engineering outreach programs for K–12 students
Bibliographic record
Abstract
Despite the recognized value of science, technology, engineering, and mathematics (STEM) education, significant barriers prevent equitable access to STEM learning opportunities, particularly for young individuals. These barriers range from limited access to quality STEM resources and insufficient support for educators to financial constraints associated with pursuing higher education in STEM fields. More profoundly, systemic issues such as stereotype threats, lack of role models, and educational disparities further exacerbate the underrepresentation of women, disabled individuals, and Black, Indigenous, and persons of color (BIPOC) in STEM disciplines (Gichuru, 2024; Klimaitis and Mullen, 2024; Mahmoud et al., 2024; Rahm and Moore, 2016). This underrepresentation leads to a diversity gap within the STEM workforce and hinders the breadth of perspectives and innovation in solving pressing global issues.Addressing these challenges requires concerted efforts to make STEM education more accessible and appealing to all young people, particularly those from underrepresented backgrounds. Ontario Tech University (OTU)’s Engineering Outreach programs respond to this need by inspiring and engaging over 40,000 young individuals annually through workshops and other events and activities focused on STEM. These initiatives are specifically designed to target young women and underrepresented groups, encouraging their pursuit of engineering careers and mitigating the barriers to STEM education. By examining the effectiveness of Ontario Tech’s Engineering Outreach programs, we can gain insights into strategies that successfully increase participation and create opportunities for underrepresented youth in STEM fields.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".