Adaptive responses to inter-group competition over natural resources: the case of leakage
Bibliographic record
Abstract
Policies create externalities. In conservation, one of the most common types of externalities is leakage, where damages are exported beyond a policy's jurisdictional boundaries. Although much research has measured leakage, little addresses its impact on the lives of people suffering from damages from leakage. This paper develops a comprehensive modeling framework to formalize the dynamics by which individuals and communities exposed to leakage adapt to the challenges posed. Specifically, we use a combination of bio-economic models, abatement curves, and other ethnographically informed analytic modeling to explore the types of damages caused, how communities can adapt to them, and the consequences of such adaptive processes. The theory points to critical system dynamics necessary to understand when and why leakage produces environmental and economic damage. Firstly, the kinds of damages imposed are fundamentally linked to the resource's health, the incentives of those committing leakage, labor market dynamics, and ecosystem services. Secondly, there is no silver bullet for communities adapting to leakage; adaptation is often costly, which means that stopping all the damages from leakage may be infeasible, requiring communities to make the best of a bad situation and allocate precious resources across various strategies. Finally, the strategies adopted to deal with leakage can have far-reaching negative and positive effects, potentially creating cyclical changes in resource stocks, cascades of further leakage or promoting strict property rights that reshape the social landscape.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".