Implicit math‐gender stereotype present in adults but not in 8th grade
Bibliographic record
Abstract
INTRODUCTION: Traditional math-gender stereotypes suggest that boys/men are more likely to enjoy and succeed in mathematics while girls/women are more likely to enjoy and succeed at language arts subjects. The usefulness of implicit measures of math-gender stereotypes has been a subject of investigation in mainly the adult research literature. This is problematic, as adults have typically already made many important decisions about their academic and professional futures, thus making it unclear as to whether implicit attitudes about mathematics causally influence men and women's participation in STEM. Therefore, it is important to assess if the same kind of implicit and explicit stereotypes are found among adolescents who have yet to make many of these decisions. METHODS: A total of 196 eighth-grade students and 80 adults participated in this study. Participants completed both implicit and explicit self-report measures of math-gender stereotype attitudes, in addition to measures of math self-concept, verbal self-concept, as well as mathematical performance. RESULTS/CONCLUSIONS: We found that adolescent boys and girls reported either in-group favouritism or egalitarian attitudes towards math and language subjects. Adult participants reported more typical math-gender stereotypes on self-report measures. Adults also demonstrated a correlation between explicit and implicit measures of math-gender stereotype, which was not the case for adolescents. Implicit math-gender stereotype measures were not a reliable predictor of any other math-related variables among adults or adolescents. These results are discussed in terms of their implications for the potential usefulness of implicit measures of math-gender stereotypes for adolescents.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 0.001 |
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".