Local innovation and initiative to strengthen owner-operator policy and build resilient and equitable fisheries access rights
Bibliographic record
Abstract
Communities can build resilience by engaging in the governance process to ensure that their access to fisheries and coastal resources is local, culturally appropriate, and equitable between and within generations. But challenges lay ahead for governing access-rights as companies and actors from outside communities have sought out new instruments to control the fisheries value chain. While much of the discussion has focused on the distributional effects of quotas, companies continue to find loopholes to exert control over licenses, traditionally design to be owned and controlled by independent fishermen. Trust agreements, product agreements, controlling agreements, and sick days and vacation transfers are among the instruments used to undermine owner operator and fleet separation policies. Taken together, these informal property arrangements are a mirror of historical struggles between fishermen, buyers, marketers and wholesalers. We report on insights collected from field work in Southwest Nova Scotia and New Brunswick, and participation in policy forums and discussions among fishermen and DFO. Fishermen’s associations have played a vital role in crafting institutional innovations that build on the legal and enforcement limitations set by DFO officials. We recommend measures that further the capacity of associations and community groups to innovate and develop flexible and locally appropriate instruments to govern access to adjacent fisheries.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".