Bibliographic record
Abstract
The allocation of individual transferable quotas (ITQs) as shares of a total allowable catch (TAC) is now widely practised in fisheries management, but is not without controversy. It is often suggested that the possession of ITQs should provide an incentive for fishers to exercise stewardship of the resource. Quota holders acting in their economic self-interest should collectively exercise stewardship, setting TACs and supporting enforcement measures to maximize the present value of future profit streams. But it is in the economic self-interest of an individual fisher possessing ITQ to take additional unreported catch, through discarding, high-grading, or quota-busting. Thus, ITQs in themselves will not prevent a “tragedy of the commons”, unless there is sufficient compliance monitoring and enforcement to deter hidden catches. ITQs, with adequate enforcement, have been demonstrated to effectively address the race to fish and result in improved sustainability and profitability. There are questions of equity concerning the flow of benefits from the allocations of quotas and associated profit streams and who pays for the management costs required to sustain them. There are also issues around the ability of ITQ-based management to address other social and environmental objectives.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".