Bibliographic record
Abstract
In the United States, Canada and the United Kingdom wage inequalities widened rapidly in the 1980s. The other developed countries seem to have experienced higher unemployment rates. Western economists indicate that economic globalization and technological changes are possible causes. But these environmental changes are exactly what Japanese manufacturing industries have experienced. They have also experienced rapid aging of their labor forces and high educational upgrading. In this paper, I carefully examine the changes in the wage structure of manufacturing industries for more than thirty years, and analyze the effects of environmental changes on them. The main results are (1) that the age-wage differentials did not shrink in spite of the recent growth of and the reduced relative demand for older workers; (2) that the college graduates/high school graduates differential shrank until the middle 1980s because of the increase in the college graduates relative supply, and that recently both the wage differential and the relative supply have been stable; (3) that the nonproduction/production differential has been shrinking over the three decades while nonproduction workers have been relatively increasing; (4) that the shrinking of the gender differential has been very modest; and (5) that the overall wage dispersion shrank greatly during the 1960s and has been shrinking continually until the present.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.014 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".