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
The authors analyze how firms of different sizes reward measured skills and unmeasured ability. The empirical methodology, based on nonlinear instrumental variable estimation, permits direct estimation of the returns to unmeasured ability by firm size. An analysis of panel data from the Canadian Survey of Labour and Income Dynamics for two periods, 1993–1998 and 1996–2001, reveals statistically significant differences between firms of different sizes. In particular, returns to unmeasured ability are higher in medium-sized firms than in either small firms or large firms. The authors find that the firm-size wage gap and the differential in returns to unmeasured ability between small and medium-sized firms is mainly explained by ability sorting. The fact that larger firms reward ability less than medium-sized firms is consistent with an explanation based on monitoring costs. When firms become “too large,” monitoring costs may prevent them from rewarding ability directly through wages.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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