Examining adolescent daughters' and their parents’ academic‐gender stereotypes: Predicting academic attitudes, ability, and STEM intentions
Bibliographic record
Abstract
INTRODUCTION: Women continue to be underrepresented in Science, Technology, Engineering, and Mathematics (STEM) and research suggests that academic-gender stereotypes can be a contributing factor. In the present research, we examined whether adolescent daughters' and their parents' gender stereotypes about math and liberal arts would predict the academic orientation of daughters at a critical time of career related decision-making. METHODS: 49), resulting in 147 mother-daughter dyads and 83 father-daughter dyads. Implicit academic-gender stereotypes were measured using an Implicit Association Test (IAT) and explicit stereotypes, academic attitudes, academic ability, and daughters' intentions to pursue a degree in STEM were measured using self-reports. RESULTS: Neither mothers' nor fathers' implicit or explicit academic-gender stereotypes predicted adolescent daughters' implicit stereotypes; however, fathers' explicit stereotypes predicted daughters' explicit stereotypes. In addition, daughters' academic orientation, a latent variable composed of adolescent girls' academic attitudes, academic ability, and intentions to pursue a degree in STEM, was predicted by daughters' own implicit and explicit stereotypes. This was the case for relative orientation toward math versus liberal arts, as well as math (but not liberal arts) orientation. CONCLUSIONS: These findings suggest the importance of challenging academic-gender stereotypes during adolescence and suggest that at this stage in development, mothers' and fathers' academic stereotypes might have limited relation to daughters' own implicit associations with academic domains.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".