Fisheries bioeconomics: why is it so widely misunderstood?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Many fisheries management systems, even when based on apparently sound science, have failed to prevent severe overfishing. And even when successful in this sense, such systems have frequently resulted in a large degree of excess fishing capacity. The reason for these failures can often be found in a lack of consideration of the economic incentives affecting fishermen. Specifically, when forced to compete for a fixed total annual catch quota (TAC), fishermen are motivated to fish at high intensity, and to expand the fishing power of their vessels. Individual fishing quotas (IFQs) are being increasingly used as a method of altering economic incentives in a desirable way. IFQ systems, however, can also suffer severe shortcomings, unless substantial fees are extracted for the exclusive right to exploit a publicly owned resource. When combined with appropriate fees, or royalties, IFQs can indeed result in sustainable, profitable fisheries. There still remains the fundamental question of risk management, but this is also now beginning to be addressed. Thus there is now a strong hope for the future success of marine fisheries, at least within 200‐mile coastal zones.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.070 | 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 it