The Reliability of the gender Implicit Association Test (gIAT) for Explaining Female−Male Differences in High-Ability Careers
Bibliographic record
Abstract
Females are outnumbered by males in many high-ability careers in the fields of academic medicine and science, technology, engineering, and mathematics (STEM). These differences are often attributed to implicit bias as measured by the gender Implicit Association Test (gIAT). Statistical p-value plots were used to independently test the ability to reproduce research claims made in relation to female−male differences in these careers. The p-value plots were developed using data sets from two published meta-analyses. One examined predictive power of the gIAT, and the other examined predictive power of vocational interests (personal interests and behaviors) for explaining female−male differences in these careers. The gIAT (implicit bias) p-value plot showed that it is unreliable for predicting female−male differences. Whereas the p-value plot for vocational interests supported these differences. Researchers of implicit bias should expand their modeling to include vocational interests and additional relevant explanatory variables. In short, these meta-analyses and the p-value plots provided no support for the gender Implicit Association Test influencing choice and female−male differences of high-ability careers.
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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 | 0.111 | 0.255 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.013 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".