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Record W2026722665 · doi:10.1017/s1537592709991861

International Cooperation as Interagency Cooperation: Examples from Wildlife and Habitat Preservation

2009· article· en· W2026722665 on OpenAlexaboutno aff
Robert Pahre

Bibliographic record

VenuePerspectives on Politics · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationAgency (philosophy)WildlifeLegislatureUnit (ring theory)Political scienceVariation (astronomy)BusinessEnvironmental resource managementInternational tradeEconomicsEcologySociologyLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.014
GPT teacher head0.265
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2009
Admission routes1
Has abstractyes

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