State Intervention to Protect Endangered Species: Why History and Bad Luck Matter
Bibliographic record
Abstract
Abstract: Illegal exploitation threatens the survival of many species, and anti‐poaching legislation (“protection on paper”) does not protect species. State enforcement is needed to support and supplement the formal status of endangered species, but state enforcement can be a source of instability leading to the demise of species if ad hoc rules are followed blindly. We demonstrate this with a model of poaching, wildlife, and government wildlife enforcement, but our findings apply more generally. Crucial assumptions of the dynamic model are that both poaching and enforcement effort increase or decrease whenever poaching effort and enforcement are relatively profitable or unprofitable activities, respectively. We found that multiple steady states may characterize the system's equilibrium. Depending on initial populations, the initial extent of state involvement, and random events, animal populations may be severely depleted or unexpectedly built up during transition phases. Our findings highlight the importance of history and luck in protecting endangered species.
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How this classification was reachedexpand
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) | 0.008 | 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 teacher head, 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".