Institutional and Ideational Features of Canadian-US Fishery Management Networks: Connectivity, Coherence, and Collaboration
Bibliographic record
Abstract
This article examines fishery management along and across the Canadian-US border through the comparison of collaborative transboundary networks in four regions: the Salish Sea, the Great Lakes, the Gulf of Maine, and the northern region including the Gulf of Alaska and the Hecate Strait. Transnational fishery management is an inter-organizational and multi-jurisdictional enterprise constituted by shared understandings of a suite of tasks and by communications among the participants. We use survey data to summarize the inter-organizational scale and participation in the networks, the centrality of different organization types, the factors that contribute to network formation, other ideational network traits like inter-organizational trust and risk perception, the activities that actors engage in to facilitate transboundary collaboration, and the influence of binational organizations. We show that the networks managing marine fisheries exhibit binodal organizational clustering along national boundaries, but the freshwater Great Lakes fishery has a more transnational multi-nodal network. These fishery binational management networks are brought together by regulatory dependencies and shared ideas about who should be involved, despite perceived risks between Canadian and US government agencies and a lack of trust in the political system to ensure fairness. When present, binational fishery commissions can facilitate collaboration by framing issues on an ecosystem scale and exercising their convening power.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".