L'eggo My Ego: Reducing the Gender Gap in Math by Unlinking the Self from Performance
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
Stereotype threat can vary in source, with targets being threatened at the individual and/or group level. This study examines specifically the role of self-reputational threat in women's underperformance in mathematics. A pilot study shows that women report concerns about experiencing self-reputational threat that are distinct from group threat in the domain of mathematics. In the main study, we manipulated whether performance was linked to the self by asking both men and women to complete a math test using either their real name or a fictitious name. Women who used a fictitious name, and thus had their self unlinked from the math test, showed significantly higher math performance and reported less self-threat and distraction, relative to those who used their real names. Men were unaffected by the manipulation. These findings suggest that women's impaired math performance is often due to the threat of confirming a negative stereotype as being true of the self. The implications for understanding the different types of threats faced by stereotyped groups, particularly among women in math settings, are discussed.
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 | 0.001 | 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 | 0.001 |
| 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