Understanding Global Supply Chains and Seafood Markets for the Rebuilding Prospects of Northern Gulf Cod Fisheries
Bibliographic record
Abstract
Although fisheries production and seafood trade are global in scope, with billions of dollars in exports, the rebuilding of collapsed fisheries often focus on national fisheries policy and management measures, with little attention to global supply chains and international consumer markets. Even with two moratoria and two decades of policy changes since the Northern Gulf cod fisheries collapsed in eastern Canada, rebuilding has stalled and the fishing industry and coastal communities continue to undergo challenges with economic viability and resource sustainability. This paper examines and analyzes the global supply chain and marketing dimension of Northern Gulf cod fisheries. Drawing upon fisheries bioeconomics and governance theory, a pre- and post-collapse analysis is undertaken to understand key drivers and institutional mechanisms along global fish supply chains for an effective and successful rebuilding. Findings indicate that the collapse of the cod fishery has cascading effects that go beyond ecosystem changes to new harvesting activities, industry restructuring, supply chain reorganization, new global markets and consumer preference for certified seafood. This suggests that a holistic rebuilding approach is necessary, one that integrates institutional and behavioral changes for both producers and consumers at various scales of fisheries production, political economy issues, as well as cross-scale policies on marine conservation and regional economic development.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".