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
Past research has assumed that group differences in academic performance entirely reflect genuine differences in ability. In contrast, extending research on stereotype threat, we suggest that standard measures of academic performance are biased against non-Asian ethnic minorities and against women in quantitative fields. This bias results not from the content of performance measures, but from the context in which they are assessed-from psychological threats in common academic environments, which depress the performances of people targeted by negative intellectual stereotypes. Like the time of a track star running into a stiff headwind, such performances underestimate the true ability of stereotyped students. Two meta-analyses, combining data from 18,976 students in five countries, tested this latent-ability hypothesis. Both meta-analyses found that, under conditions that reduce psychological threat, stereotyped students performed better than nonstereotyped students at the same level of past performance. We discuss implications for the interpretation of and remedies for achievement gaps.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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