Interagency Coordination among Wildlife Management Agencies in the Presence of Source-Sink Population Dynamics
Bibliographic record
Abstract
Including Alaska, more than one-fourth of the US land area is federally owned. Studies of the political economy of this land management are underdeveloped, and our understanding of transboundary coordination between different units is virtually nonexistent. Transboundary issues are especially important for managing mobile resources (wildlife) or resources with externalities (timber and watersheds). This paper provides a first cut at understanding these issues with a model of agency decisions against a background of population biology. The paper defines agency objective functions in wildlife management in terms of mandates (or decision rules), including non-intervention, population recovery, and sustainable harvest. Combining mandates and populations yields a large number of possible transboundary cooperation problems, several of which I analyze in depth. The model yields insights into why transboundary cooperation within and between the US and Canada has been successful for migratory and anadromous species such as salmon, elk, and caribou, but unsuccessful in managing most endangered species and game animals. It also explains success or failure in state-federal and state-tribal coordination problems as well as large-scale management challenges such as the
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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) | 0.000 | 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 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".