Pobreza, degradación ambiental y liberalización del comercio: Hacia una intervención de políticas «second best»
Bibliographic record
Abstract
Forest based agricultural systems in the tropics are being opened up to international trade at an unprecedented rate.This is the case of tropical agriculture in Mexico under the North American Free Trade Agreement (NAFTA), which is also having significant impacts on the decentralized land use decisions of small-scale farmers and on the natural resource base on which they depend. This paper develops a bioeconomic model of a typical forest-land based farming system that is integrated with the non-farm labour sector, as typically found in tropical regions. The data used to generate the simulations were gathered in two communities of Yucatan (Mexico) in 1998-2000. Through a systemdynamics framework, the agro-ecological and farming economic subsystems are integrated and the current situation of price liberalization that is negatively affecting soil capital and income levels is compared to a scenario that precludes an «optimal path to extinction» through careful policy intervention. This second-best case is based on a targeted policy mix that seeks to maintain the system viable for as long as possible above an irreducible poverty level. The policy intervention involves, simultaneoulsy, subsidizing off-farm wage rates, intensification of land use, and the control of households’ rights to the forest commons. The model shows that such policy intervention can result in a large positive discounted net payoff basedon the increased incomes for the farming community after deducting the implementation costs of such intervention.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".