Real consequences of the necessity of digitalization in rural Mexico
Bibliographic record
Abstract
The export of agricultural products is currently Mexico's most important source of foreign exchange, exceeding revenues from oil exports and tourism. Historically, agricultural export chains have been formed since colonial times, e.g. tobacco, coffee, bananas, cocoa or cotton. Avocados, berries and fresh vegetables, as well as alcoholic beverages (beer and tequila) are at the forefront currently. The target country is primarily the United States of America. Today's rural Mexico faces new challenges because it must comply with obligations derived from the T-MEC agreement, which was signed on July 1, 2020. It was preceded in 1994 by the North American Free Trade Agreement (NAFTA) between the United States, Mexico and Canada, which created the largest free trade region in the world. One of the main points of the agreement is to support the digitization of international trade and strengthen consumer protection with complete data at every stage of the production chain. An important point for agricultural workers is also the commitment to strengthen and expand the protection of workers' rights. The fulfillment of these obligations can be translated as greater supervision of companies will be able to meet such demands? Does the agreement ultimately lead to a greater concentration of access to water and land in the hands of large firms with foreign capital? Are SMEs (small and medium enterprises) still able to export? What challenges does the rural labor market face?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".