Bibliographic record
Abstract
Proponents of what has been termed the Gender Similarities Hypothesis (GSH) have typically relied on meta-analyses as well as the generation of nonsignificant tests of mean differences to support their argument that the genders are more similar than they are different. In the present article, we argue that alternative statistical methodologies, such as tests of equivalence, can provide more accurate (yet equally rigorous) tests of these hypotheses and therefore might serve to complement, challenge, and/or extend findings from meta-analyses. To demonstrate and test the usefulness of such procedures, we examined Scholastic Aptitude Test–Math (SAT-M) data to determine the degree of similarity between genders in the historically gender-stereotyped field of mathematics. Consistent with previous findings, our results suggest that men and women performed similarly on the SAT-M for every year that we examined (1996–2009). Importantly, our statistical approach provides a greater opportunity to open a dialogue on theoretical issues surrounding what does and what should constitute a meaningful difference in intelligence and achievement. As we note in the discussion, it remains important to consider whether even very small but consistent gender differences in mean test performance could reflect stereotype threat in the testing environment and/or gender biases in the test itself that would be important to address.
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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 | 0.038 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".