Diferencias regionales de las primas por calificación en la China urbana. Repercusiones en el crecimiento y en la igualdad
Bibliographic record
Abstract
Resumen A partir de datos de encuestas de hogares, los autores observan que las primas por calificación aumentaron en toda China entre 1995 y 2002, pero solo en las provincias costeras entre 2002 y 2007, año en que estas también registraron mayor desigualdad salarial y contribuyeron más a la desigualdad salarial urbana total. Según un modelo de efectos fijos estimado, la privatización explica la evolución del primer periodo, y la integración de China en la economía mundial, la del segundo. Reducir la desigualdad exige, según los autores, la reforma del Registro de Población Hukou, que obstaculiza la movilidad de los trabajadores calificados y posiblemente también el crecimiento.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".