Agricultural subsidies: cutting into forest conservation?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We examine how agricultural subsidies may induce deforestation and interact with conservation programs by analyzing two large-scale national programs in Mexico that have existed simultaneously for more than a decade: an agricultural subsidy for livestock (PROGAN) and a program of payments for ecosystem services (PES). Looking across the entire Mexican landscape, we exploit the surprises in the timing of enrollment in PROGAN's waves, fluctuations in program payments, and the change in the value of the subsidy induced by inflation and currency fluctuations to identify the impacts of the livestock subsidy on environmental outcomes. We find that PROGAN increased municipal deforestation by 7 per cent. The deforestation effects of PROGAN were smaller in municipalities with higher concentrations of PES recipients. We suggest that livestock subsidies could be better targeted to places with low deforestation risk and high livestock productivity to maximize food production and minimize negative externalities caused by deforestation.
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.
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) | 0.001 | 0.001 |
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 it