Bibliographic record
Abstract
Past research documents a clear link between gender role norms and young men and women’s interest in divergent careers. Generally, individuals are more likely to take on careers that they perceive as normative for their own gender. However, past research has focused primarily on heterosexual populations, and has not considered in depth whether mainstream norms might have different, possibly weaker, effects on career decisions among LGBTQ+ populations. There is some preliminary evidence suggesting that non-heterosexual individuals endorse less traditional gender roles and might make less gender-normative career choices themselves. In a sample of heterosexual vs. non-heterosexual individuals from 46 countries (after exclusions), we collected data on participants’ sexual orientation, their own injunctive gender role norms of who should enter HEED and STEM careers, their perceptions of others’ injunctive norms for HEED and STEM careers, and well as their own interest in these careers. We examine whether non-heterosexual (compared to fully heterosexual) individuals endorse less traditional gender role norms and show a weaker relationship between their perception of mainstream norms and their own career interests – possibly especially in countries where homosexuality is relatively highly accepted (i.e., in countries where homosexuality is not accepted, non-heterosexual people might still stick to highly stereotypic career choices). The attached document details all our key hypotheses and analysis plans.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.004 |
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".