Seeing women who fit: Girls’ forecasted fit in STEM fosters career interest
Bibliographic record
Abstract
Girls often express less interest in STEM (science, technology, engineering, mathematics) education and careers than boys, despite having comparable aptitude. We randomly assigned 242 girls ( Mdn age = 12 years; 38% East Asian; 37% White) at Canadian STEM camps to control conversations about generic camp experiences, or intervention conversations where STEM role models discussed how STEM education and careers align with each girl’s most important value and emphasized the social community inside and outside of STEM. Key measures were collected at baseline, and several days after the intervention (or control). Girls’ current STEM fit did not differ by condition, but as expected, the intervention (vs. control) significantly improved girls’ forecasts regarding future STEM fit ( ds = 0.27–0.35) and girls’ interest in STEM careers ( d = 0.42), with a marginally significant boost in girls’ interest in STEM high school classes ( d = 0.23). Pre-post increases in forecasted STEM fit mediated increases in STEM interest. Forecasted fit (beyond current fit) appears pivotal for promoting girls’ sustained interest in STEM.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".