Bibliographic record
Abstract
Advocates for recreational fishing, public servants charged with fisheries management, and scientists and other experts who provide objective advice, all need to understand the nature and dimensions of fisheries politics.Accusing someone of “playing politics” usually is intended as a criticism, even an insult. But politics is the social process by which differences are expressed and resolved. If you don’t have differences, then you don’t have politics. A political situation, whether it is in a family, the workplace, government administration or a contest for public office is the process through which differences are discussed and settled.Fisheries politics takes place at many levels. It determines the resources available to manage fisheries and understand their impacts. It defines the relationship between conservation and extraction. It determines the allocation of harvest between competing interests. It sets the international rules between nations for the conservation and sharing of migratory and straddling stocks.Underlying these political relationships are rules and norms of political behavior that can be learned and practised by those who wish to maximize their influence over how fisheries are managed and practised.Canada’s West Coast provides a useful example of efforts by the Canadian government to facilitate fisheries politics by providing structures and processes within which different interests can contribute to the politics of fisheries management. A participant-observer brings his perspective as both an ardent angler and a political scientist specializing in the relationship between interest groups and government to suggest some rules for effective engagement in fisheries politics.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".