Extracting spatial resources under possible regime shift
Bibliographic record
Abstract
How will countries harvesting mobile renewable resource react to the threat of climate change? We address the non-cooperative exploitation of a migratory renewable resource in the presence of possible regime shift that affects its movement. Motivated by the anticipated effects of climate change, we model a regime shift that will alter the spatial movement patterns of the resource at some point in the future. We develop a stochastic spatial bioeconomic model to address the effects of this class of regime shift on non-cooperative harvest decisions made by decentralized owners such as countries exploiting a migratory fish or other natural resource stock. We find that the threat of a future shift modifies the standard golden rule and may induce more aggressive harvest everywhere, irrespective of whether the owner will be advantaged or disadvantaged by the shift. We also identify conditions under which the threat of regime shift induces owners to reduce harvest rates in advance of the shift. Our analysis suggests that different property rights structures (single ownership vs common property) or heterogenous growth can give rise to previously unexplored incentives and can even reverse conventional wisdom about how countries will react to the threat of environmental change.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".