Desajuste educativo y ajuste económico: ¿cómo respondió el mercado de trabajo mexicano ante la pandemia?
Bibliographic record
Abstract
Due to the arrival of COVID-19 in Mexico, a majority of the population was affected in terms of their labor participation. This article aims to measure the levels of educational mismatch—overeducation and undereducation—in Mexico before and after the closure of activities decreed during the pandemic. The research examines changes in educational adjustment from the first quarter of 2020 to the third quarter of the same year and the first quarter of the following year, and it investigates whether there were modifications in sociodemographic profiles, changes in the conditions of those experiencing educational mismatch, and if these dynamics were a result of the crisis. The analysis uses the National Occupation and Employment Survey and employs multinomial models to assess the probability of being in some form of educational mismatch, with a grouped base for five survey editions to establish changes over time. The results show an increase of approximately 0.5% in overeducation in the total occupied population, considering sociodemographic and labor insertion characteristics. Similarly, there are differences between informal and formal employment in terms of how these changes occur, with the former showing increases first, followed by the latter.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".