IMPACTS OF COVID-19 IN THE AGRI-FOOD SECTOR OF MEXICO: METHODOLOGIES AND ANALYSIS TOOLS
Bibliographic record
Abstract
Abstract: This article estimates the impact on the agrifood sector of the SARS-Cov-2 coronavirus (Covid-19) and identifies socioeconomic characteristics of those infected in the Mexican Republic. The pandemic has exacerbated the country's problems and the macroeconomic consequences are considerably negative; with emphasis on the most vulnerable sectors. In Mexico, according to data from the National Survey of Occupation and Employment for the "first quarter of 2020, there are 6.5 million people engaged in agricultural work" (INEGI, 2020); of these 5.8 million are men and 0.77 million are women, with an average age of 41.7 years and schooling of 5.9 years; and of every 100 workers, 24 speak indigenous language. In this population 2,381,294 people are of vulnerable age to survive COVID-19 (50 years and over), being 89.54% men and 10.46% women. This population could be affected by the pandemic by 35%, as it is a vulnerable population by age and presents chronic-degenerative diseases. This would leave the agricultural sector in a possible production crisis, thus generating an increase in the prices of products from this sector and would force a strong dependence on imports.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".