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
Research on the mechanisms that reproduce social class advantages in the United States focuses primarily on formal schooling and pays less attention to social class discrimination in labor markets. We conducted a résumé audit study to examine the effect of social class signals on entry into large U.S. law firms. We sent applications from fictitious students at selective but non-elite law schools to 316 law firm offices in 14 cities, randomly assigning signals of social class background and gender to otherwise identical résumés. Higher-class male applicants received significantly more callbacks than did higher-class women, lower-class women, and lower-class men. A survey experiment and interviews with lawyers at large firms suggest that, relative to lower-class applicants, higher-class candidates are seen as better fits with the elite culture and clientele of large law firms. But, although higher-class men receive a corresponding overall boost in evaluations, higher-class women do not, because they face a competing, negative stereotype that portrays them as less committed to full-time, intensive careers. This commitment penalty faced by higher-class women offsets class-based advantages these applicants may receive in evaluations. Consequently, signals of higher-class origin provide an advantage for men but not for women in this elite labor market.
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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it