Public Policies Shaping Mexican Small Farmer Practices and Environmental Conservation: The Impacts of 28 Years of PROCAMPO (1994–2022) in the Yucatán Peninsula
Bibliographic record
Abstract
Conditional cash transfer (CCT) programs, generally viewed as policies to modernize and increase agricultural production and commercialization, also have social and environmental impacts. Among the first Mexican CCT programs, PROCAMPO is directed toward traditional agriculture and pays farmers for permanent cultivation, ignoring traditional fallow systems. It was implemented nationally in 1994 to counteract the effects of trade liberalization. Its objectives encompassed modernizing and improving agricultural competitiveness and environmental conservation. Here, we analyze PROCAMPO from the perspective of environmental conservation to understand its effects on agricultural practices and forest cover, specifically in the Yucatán Peninsula, where agriculture sustainability was previously achieved via an alternating cycle of multi-crop system (milpa) and forest. We performed an in-depth program analysis, reviewing 51 documents, including scientific literature, technical evaluations, and official records. Research consistently showed direct effects of PROCAMPO on agricultural practices resulting in extensive land use change, including a reduction in crop diversity and the elimination of traditional milpas and fallow. PROCAMPO has impacted conservation by causing high rates of deforestation. Our findings show the need to reorient the design and implementation of agricultural policy to increase agroecosystem resilience and ecological service provision to face climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".