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
In this article we argue that wage inequality and occupational mobility are intimately related. We are motivated by our empirical findings that human capital is occupation specific and that the fraction of workers switching occupations in the U.S. was as high as 16% a year in the early 1970's and had increased to 21% by the mid-1990's. We develop a general equilibrium model with occupation-specific human capital and heterogeneous experience levels within occupations. We find that the model, calibrated to match the level of occupational mobility in the 1970's, accounts quite well for the level of (within-group) wage inequality in that period. Next, we find that the model, calibrated to match the increase in occupational mobility, accounts for over 90% of the increase in wage inequality between the 1970's and the 1990's. The theory is also quantitatively consistent with the level and increase in the short-term variability of earnings.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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