Bridging Jurisdictional Divides: Collective Action Through a Joint Strategic Plan for Management of Great Lakes Fisheries
Bibliographic record
Abstract
Ontario, eight Great Lakes states, U.S. tribes, federal agencies in Canada and the United States, and the binational Great Lakes Fishery Commission all have a role in Great Lakes fishery management, with the non-federal governments retaining primary management authority. This dissertation is about how and why independent (yet interdependent) fishery managers work collectively through A Joint Strategic Plan for Management of Great Lakes Fisheries, a non-binding agreement. This research focuses on what the Joint Strategic Plan means to those who participate in the process and relies primarily on semi-structured interviews and participant observation to address the central questions: Why do fishery managers take collective action in Great Lakes fishery management? What do fishery managers hope to achieve when they participate in the Joint Strategic Plan? ** The data reveal four facets of Great Lakes fishery management that help explain how and why collective action occurs. First, the history of Great Lakes fishery management illustrates that the non-federal governments have a strong sense of jurisdictional independence, which has made them sensitive to usurpation of their authority and thus somewhat reluctant to cooperate with each other. Second, fishery managers are part of an “epistemic community,” a group of like-minded professionals, and the Joint Strategic Plan gently coerces this community into working together and substantially rewards them for doing so. Third, despite long-standing tensions between the federal and non-federal governments, the non-federal members generally trust their federal counterparts and work with them synergistically. Finally, members reject the idea of a binding agreement because it would be inconsistent with Great Lakes fishery governance and because they feel they can achieve their goals through a non-binding agreement. These conclusions are applied to a case study—a dispute over walleye harvest in Lake Erie in 2004—as a way to illustrate how members believe the plan serves their needs even in stressful situations. This dissertation concludes by identifying four overarching themes related to fishery governance through the plan—jurisdictional independence, shared strategies and plans, science, and personal relationships—and discusses how those factors relate to the plan’s durability and replicability.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".