Bibliographic record
Abstract
The principal aim of national oceans policymaking is to identify and assess all current and future uses of ocean spaces and resources in order to facilitate the making of effective management arrangements for them. There are a myriad of actual and potential uses of the oceans. They can lead to a range of potentially conflicting interests across different sectors. This means that it is a daunting and politically fraught task to integrate management of all ocean uses. Fisheries is a key sector that must feature in any effective national oceans policy. This is because fishing is the most intensive use of marine space and resources and the fishing industry is a key contributor to many national economies. A challenge for the incorporation of fisheries interests in national oceans policymaking is that fisheries is a diverse sector. In addition to commercial fishing, it includes recreational fishing and, in some countries, indigenous and subsistence fishing. Fishing also has an international and regional dimension and it overlaps with aquaculture. This article identifies the range of fisheries interests and considers how they are incorporated into national oceans policymaking, focusing on Australia and Canada.
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 | 0.078 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".