Diferencias regionales y capital humano: Examinando las brechas salariales de los individuos en Colombia
Bibliographic record
Abstract
This research quantifies regional differences in the returns on human capital variables in Colombia and attempts to isolate the effects of them on the wages of individuals. This was done using the techniques of decomposition of Oaxaca-Blinder and Junh, Murphy and Pierce, using information from the Great Integrated Household Survey in the second quarter 2008. It found that compared with Bogota, Medellin has the smallest difference in wages of individuals, while Pasto is at the example in other side, in turn explaining that Medellin is not in the condition of belonging to the region. The initiative will focus on modeling and measuring the City Premium is valid to the extent that in 10 of the 12 metropolitan areas found that the residual factor decomposition technique (U) helps to widen the gap in income individuals with respect to Bogota. Bearing in mind that these techniques have been little used for regional analysis, this research pretends make a contribution in that way.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".