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
We examine the extent to which declining manufacturing employment may have contributed to increasing inequality in advanced economies. This contribution is typically small, except in the United States. We explore two possible explanations: the high initial manufacturing wage premium and the high level of income inequality. The manufacturing wage premium declined between the 1980s and the 2000s in the United States, but it does not explain the contemporaneous rise in inequality. Instead, high income inequality played a large role. This is because manufacturing job loss typically implies a move to the service sector, for which the worker is not skilled at first and accepts a low-skill wage. On average, the associated wage cut increases with the overall level of income inequality in the country, conditional on moving down in the wage distribution. Based on a stylized scenario, we calculate that the movement of workers to low-skill service sector jobs can account for about a quarter of the increase in inequality between the 1980s and the 2000s in the United States. Had the U.S. income distribution been more equal, only about one tenth of the actual increase in inequality could have been attributed to the loss of manufacturing jobs, according to our simulations.
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 | 0.000 | 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) | 0.001 | 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