The Temporal and Directional Relationship Between Group-Level Implicit and Explicit Gender Bias
Bibliographic record
Abstract
Explicit and implicit gender-science and gender-career biases have shifted toward neutrality in the past decade. Researchers speculate that these changes result from women's increased visibility in the science field and job market, but little is known about how the changes in group-level explicit and implicit gender-science and gender-career bias relate to one another over time. Building on contemporary models of group-level bias, this study investigates the temporal and directional relationship between group-level implicit and explicit gender bias between 2007 to 2016 using multivariate multilevel modeling. We found that lower group-level implicit bias in a previous month predicts lower group-level explicit bias in the following month. We also found evidence that group-level explicit bias in a previous month was positively associated with group-level implicit bias in the following month. These findings have practical and theoretical implications for understanding the bidirectional relationship between group-level implicit and explicit biases over time.
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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 | 0.003 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".