International Cooperation as Interagency Cooperation: Examples from Wildlife and Habitat Preservation
Bibliographic record
Abstract
Cooperation between two agencies presents much the same problem whether these agencies are found in different countries or in the same country. This similarity is generally overlooked because the issues over which agencies negotiate often differ—defense and trade policy at the international level, transportation or land use at the domestic level. Demonstrating the analytical similarity of international cooperation to domestic interagency cooperation requires holding issue area constant while allowing interstate and intrastate units to vary. To do this, I focus on cooperation over wildlife and habitat preservation at the domestic and international levels in the US and Canada. I explain this variation in cooperation in a simple theory in which agency goals and certain features of species interact. Variation between successful and unsuccessful cooperation in this issue area is governed solely by characteristics of the species and agency goals in each management unit, and does not depend on whether a problem is “international” or “domestic.” For scholars who think in terms of nation-states interacting in an anarchic international system, this points to a very different unit of analysis. For those who emphasize the domestic politics of international cooperation, this moves us away from executives constrained by legislatures to look at sub-units within each executive.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".