The Contemporary Landscape of Gendered Personality Traits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Associations that individuals make between a personality trait and its level of assumed masculinity or femininity are a prominent thread across the gender and politics literature. Yet, whether an attribute is categorized by scholars as “masculine” or “feminine” continues to be determined by studies that are decades old, and which often initially rested on scholarly assumptions rather than testing. Inaccurate assumptions may drive incorrect conclusions about the ways in which gendered associations influence voter and party decision-making, and contribute to ongoing gender bias in leadership evaluations. We use an original survey of 26 commonly-cited character traits associated with leadership, sampling 1,417 respondents in Australia, Canada, New Zealand, the United Kingdom, and the United States of America. We find evidence that scholarly categorization of the gendered associations diverges from how respondents rated many of those traits, and offer a modified list of gender-associated traits for researchers.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it