Fisheries Governance: The Search for Effective Management and the Illusion of Control
Bibliographic record
Abstract
The fish we catch are a very small part of all creatures that live in the oceans. Once put on land many fish products circulate in complex auction, processing, distribution and consumption patterns. The history of ‘governance’ of marine fisheries includes cases that are considered clear successes—the global effectiveness of the International Whaling Commission—and others resulting in abject failure like the cod fishery near Newfoundland; most documented cases seem to straddle somewhere in between, a fisheries purgatory. This essay suggests that the outcome of our focus in the recent past: i.e., to privatise fishing rights in mostly advanced economies and apply theories and markets maximizing single objectives has been a mixed bag. To better address evolving energy efficiency requirements, strong demands to protect the marine environment and coastal communities, and international political developments an approach will be required in the future whereby multiple parties are given more responsibility to negotiate a politically acceptable consensus defining the—short and long-term—future of the sector and its governance.
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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.068 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".