The role of stereotype vulnerability and belongingness on university students' commitment to their academic major
Bibliographic record
Abstract
The current study examined factors that are involved in university students' commitment to their academic major, with specific interest in exploring the role of stereotype vulnerability and belongingness in women's persistence in their studies. Women represent the majority of university students in Canada, but they are still underrepresented in fields of science, technology, engineering, and mathematics (STEM) due to a combination of recruitment and retention issues. The current study found that women report being more vulnerable to stereotypes than men, and that women in STEM majors report higher rates of vulnerability than women in non-STEM majors. Unexpectedly, increased vulnerability to stereotypes was found to increase one's academic commitment. Stereotype vulnerability was shown to mediate women in STEM majors' commitment to their academic major. Interestingly, the more vulnerable women were, the more committed they were to their major. This seemingly counterintuitive finding seemed driven by the negative costs women associated with withdrawal from their major. While belongingness was associated with higher levels of commitment, its role as a mediator between gender and commitment was not significant.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".