Co‐management without involvement: the plight of fishing communities
Bibliographic record
Abstract
Abstract This paper discusses the role of fishing communities in the stewardship of their adjacent fish resources, and the benefits associated with community participation in co‐management. Contrary to the view of most fisheries management agencies, local communities are able to design institutions that can successfully restore equity and limit access to the fishery. The dismissal of local concerns may be at the root of biological and social crises in fisheries, and the privatization of common fishing rights world‐wide through individual transferable quotas (ITQs) is contributing to these problems. Community involvement that is embedded into a network of management at larger spatial scales would allow fishing communities to regain some control over their livelihoods. Meaningful co‐management arrangements must go beyond consultation by redirecting the flow of social and economic benefits from the fishery back into communities. Unless geographically defined communities are allowed to share power and responsibility with government fisheries managers, both fish stocks and fishing as a way of life are in danger of vanishing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".