Addressing the Role of Climate Change in Agriculture and Mexico-US Immigration
Bibliographic record
Abstract
Among the greatest threats of climate change is the significant impact on mass displacement, particularly as it relates to Mexico-US immigration. Low crop yields from worsening climate conditions have been linked to increased migration of Mexican farmers. With a projected 4.2 million additional migrants in the foreseeable future, it poses a contemporary environmental, social, and political dilemma. This policy brief analyzes several provision proposals to be adopted into the United States-Mexico-Canada Agreement (USMCA), as evaluated under economic cost, equity, environmental impact, and feasibility criteria. My research concludes that the most effective and direct provision proposal is the implementation of adaptive farming to protect small-scale farmers against the adverse effects of climate change. The policy benefits both American and Mexican governments by mitigating financial losses from low crop yield, limiting Mexico-US immigration, and building climate resilience for farmers. This serves as a model for addressing the global increase of climate refugees and can help predict climate change-driven migration patterns in rural areas around the world.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".