La población jornalera agrícola migrante en tiempos de pandemia en México
Bibliographic record
Abstract
The presence of COVID-19 forced companies and the government to fire workers or return them home, but this measure was not applied to the migrant agricultural laborer population who go to work in export agriculture. This article tries to show that the imbalances in working conditions already existed before the pandemic, despite the fact that employment among agricultural day laborers was not significantly reduced, working conditions deteriorated sharply. It is based on the hypothesis that the increase in the deterioration of working conditions occurred not because of the impacts of COVID-19, which reduced migration from the expelling States to export agriculture, but rather that employers, taking advantage of the contraction in the employment of non-agricultural activities and the increase in the labor supply, intensified the precarious conditions of work for those employed in agriculture. The methodology used is based on the records of the National Survey of Occupation and Employment, ENOE, third quarter of 2018, 2019 and 2020, before and with the pandemic. The finding of the work is that it is shown with official statistics that working conditions deteriorated strongly, critical conditions of occupation increased, wages and income were reduced and the working day increased for internal migrant agricultural day laborers in Mexico when comparing the behavior of the employment of day laborers before and in the pandemic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".