Bibliographic record
Abstract
Previous research has revealed that moral values, pertaining to loyalty, authority, and purity, exhibit a biannual pattern of seasonal variation, with highest endorsement in spring and fall, and lowest endorsement in summer and winter (Hohm et al., 2024). Similarly, a broad human value, pertaining to conformity, exhibits a parallel biannual seasonal pattern (Bazaz et al., in prep). Because these values inform attitudes regarding gender (Lomazzi & Seddig, 2020), there might also be seasonal cycles in gender-role attitudes. The proposed research will examine whether implicit and explicit gender-role attitudes vary with the seasons. Two datasets will be acquired through Project Implicit between 2003 and 2024. One dataset (N = 876,088) provides both implicit and explicit measures of the extent to which U.S. residents stereotypically associate different gender categories with STEM careers vs. arts careers. The other dataset (N = 1,284,519) provides implicit and explicit measures of the extent to which U.S. residents associate gender categories with traditional gender roles (pursuing a career vs. raising a family). We will use harmonic regression analyses to model seasonal effects in these measures, and will test replicability across years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".