Exploring female-identifying student participation in robotics and post-secondary disciplinary interests.
Bibliographic record
Abstract
Science, technology, engineering, and mathematics (STEM), as a whole, has seen an in- crease in female participation within recent years. The same trend is not apparent within engineer- ing, with female-identifying student enrolment in post-secondary engineering programs remaining proportionally low. Current research shows little understanding between high school experiences and interest in engineering, especially within the female-identifying population. This study aims to explore female-identifying participants in high school robotics and their post-secondary plans. To fill the gap in research, a mixed-method survey with embedded design, employing Likert scales and short-answer questions, was distributed to female-identifying robotics participants in Ontario Independent Schools. The results of the paper suggest that participation in robotics does not necessarily denote an interest in post-secondary engineering but that the skills and support gathered from robotics are applicable to many related post-secondary disciplines of choice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".