The avoidance of unwanted catch and cooperation: the case of the British Columbia groundfish trawl fishery
Bibliographic record
Abstract
Abstract This paper focuses on a particularly successful avoidance of unwanted catch of protected, endangered, and threatened (PET) species in the form of ecologically important sponge and coral, to be found off Canada's Pacific coast. The fishery causing the unwanted catch—the British Columbia groundfish trawl fishery. A campaign to protect the sponge/coral led by environmental NGOs (ENGOs) resulted in the industry's access to the key California market being threatened. For reasons explained, the national resource manager's ability to take effective direct action had become severely compromised. The groundfish trawl fishing industry responded to the economic threat with a bottom up approach to the unwanted catch problem, by negotiating a habitat agreement with a consortium of ENGOs, with the blessing and full support of the national resource manager. The agreement, now in its tenth year of operation, has proved to be a remarkable success in avoidance of unwanted catch. The paper argues that the success rests fundamentally upon the fact that the fishers have been and are playing a stand alone stable cooperative game, which has led them in turn to play stable cooperative games with both the national resource manager and the ENGO consortium. The paper analyses the factors leading to the stand alone stable cooperative fisher game, doing so by necessity through the lens of game theory.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".