The importance of complexities of scale and context in fisheries research and management
Bibliographic record
Abstract
Abstract Problems with spatial (geographical) and temporal scales in fisheries research and management have become better known over the past few years. However, technological and some institutional scales, along with essential contextual dimensions (policy, intellectual, and academic) are also important. We discuss fisheries management in general with respect to these matters and their interactions. We also provide recommendations for addressing these issues, both in general and with particular reference to local fisheries. These are: (1) recognize the importance of fishers’ knowledge across all scales; (2) recognize fishers’ motivations, especially at the local/community scale; (3) thus expand the nature of the information used for management; (4) match the spatial management scales to those of the fish and the fishers; (5) recognize the limitations of large institutions to manage fisheries at local scales; (6) recognize the limits of time-series data; and (7) develop better indicators for fishing effort.
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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 | 0.079 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".