Look who’s talking: contributions to evidence-based decision making for commercial fisheries in Atlantic Canada
Bibliographic record
Abstract
Fisheries are complex social–ecological systems, meaning that the achievement of 'sustainable' fisheries demands a multifaceted approach, involving ecological, economic, social, and institutional dimensions. Addressing these diverse concerns requires the collection and synthesis of new sources of information by science-advising bodies and the engagement of multiple knowledge types from rightsholders and stakeholders. The 'modernized' (2019) Fisheries Act in Canada allows for a diverse range of considerations to form the basis of fisheries management decisions, including knowledge from community or industry groups, but it remains unclear where and when such information is available, how this information is prioritized, who contributes to information-gathering processes, and what the management consequences of information use might be. The present study uses a selection of science-advising documents and briefing notes for decision makers to explore the information and priorities informing fisheries management decisions in Atlantic Canada, with a focus on how rightsholders and stakeholders contribute to the process. These findings can inform efforts to adopt an inclusive and participatory approach to evidence-based decision making to achieve sustainable fisheries, in the broadest sense of the word.
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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 | 0.221 | 0.428 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.020 | 0.024 |
| Scholarly communication | 0.033 | 0.011 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.006 | 0.013 |
| 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".