Bibliographic record
Abstract
The enrollment of women in science, technology, engineering and mathematics (STEM) continues to be a problem across most post-secondary institutions in North America.In 2009, American universities reported 17.9% female enrollment in engineering 1 , while Canadian universities reported 17.7% in 2010 2 .While concerns around enrollment encompass numerous issues, many students, particularly females, lose interest in STEM domains as early as grades 4/5/6 3,4,5 .In this paper, we demonstrate how integrating STEM classroom content and crosscurricular aspects using creative, engineering design techniques can change student perceptions of gender within STEM fields.We designed a series of creative projects that combine mandated science, mathematics, technology, English, social studies, physical education and fine arts courses with basic electrical engineering concepts.These projects were led across five schools by one of the female researchers 6 .Over 350 local grade 5 students participated in the projects.Impressions held by students towards STEM were measured through quantitative surveys and qualitative interviews, both before and after the completion of the projects.These results are summarized in Table I.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".