Bibliographic record
Abstract
Abstract The legal and political difficulties of managing fish stocks that straddle both national waters and the high seas were not abolished by the introduction of exclusive economic zones. Here, chapters explain the wave of bitter disputes that arose in the 1990s over such straddling stocks. They show how regional responses to those challenges shaped the negotiation of a 1995 UN Fish Stocks Agreement and helped strengthen the global high seas fisheries regime. Keen attention is paid to whether and how evolving regimes meet the scientific, regulatory, and compliance-related tasks of effective management — and the significance of regime interplay in this regard. Certain developments in international fisheries law, particularly crucial to effective management of high seas fisheries, are examined: reconceptualisation of the freedom of the high seas; legal measures to control the harvesting of vessels flying flags-of-convenience; the dispute settlement apparatus; and emerging procedures for compliance-control activities by others than the flag state. These global developments are related to six regional case studies featuring management of straddling stocks in the Grand Banks of Canada, the Southern Ocean, the Doughnut Hole of the Bering Sea, the Peanut Hole of the Okhotsk Sea, the Loophole of the Barents Sea, and the Banana Hole of the Northeast Atlantic.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".