Conditions associated with protected area success in conservation and poverty reduction
Bibliographic record
Abstract
Protected areas are the dominant approach to protecting biodiversity and the supply of ecosystem services. Because these protected areas are often placed in regions with widespread poverty and because they can limit agricultural development and exploitation of natural resources, concerns have been raised about their potential to create or reinforce poverty traps. Previous studies suggest that the protected area systems in Costa Rica and Thailand, on average, reduced deforestation and alleviated poverty. We examine these results in more detail by characterizing the heterogeneity of responses to protection conditional on observable characteristics. We find no evidence that protected areas trap historically poorer areas in poverty. In fact, we find that poorer areas at baseline seem to have the greatest levels of poverty reduction as a result of protection. However, we do find that the spatial characteristics associated with the most poverty alleviation are not necessarily the characteristics associated with the most avoided deforestation. We show how an understanding of these spatially heterogeneous responses to protection can be used to generate suitability maps that identify locations in which both environmental and poverty alleviation goals are most likely to be achieved.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".