Cooperative Management Of Hake And Roe-Herring Fisheries In British Columbia
Bibliographic record
Abstract
In this paper we look at cooperation in fisheries as an alternative for fishery management. Two fisheries on the Pacific Coast of Canada that have been managed by some form of cooperation are analysed. Of particular interest is that cooperation has emerged in both the Pacific hake fishery and in the roe-herring fishery without intervention from the Department of Fisheries and Oceans (DFO). In the paper we bring out the differences between the two fisheries and see why, in the hake fishery, cooperation removes the incentive for fishermen to race for the fish, while in the roe-herring fishery cooperation does not affect this incentive. We use economic theory of cooperation to find the conditions necessary for spontaneous cooperation to come about. The most important factor is found to be reprocity, i.e., the ability to punish those who defect from the cooperative solution. The we apply the theory to the two fisheries at hand and see that it is, in fact, reciprocity that maintains the observed cooperation. In the hake fishery this cooperative behaviour is effective in eliminating the need to race. However, additional measures by the DFO are needed in the roe-herring fishery. Cooperation is found to be a possible alternative in fisheries management. However it is not a panacea. KEYWORDS fishery management, roe-herring, Pacific hake, cooperation
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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 | 0.001 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".