Impediments to fisheries recovery in Canada: Policy and institutional constraints on developing management practices compliant with the precautionary approach
Bibliographic record
Abstract
The status of many Canadian fisheries is poor, a consequence of inadequate implementation of sustainable fishery policy within the context of the Precautionary Approach (PA). A key component of implementation lies with the provision of science advice. Scientists are responsible for advising on options likely to meet policy intent and objectives. Here, we examine PA-compliance in the role of science in Canada's fisheries management decision-making. We distinguish science- based from science- determined decisions and processes. Science-based decisions emerge from consultation processes involving stakeholders; science need not always have a clear and accountable role that can be transparently separable from other inputs. Science-determined decisions result from impartial, publicly available, peer-reviewed scientific determinations clearly distinguishable from other inputs. Our findings are consolidated with a comparison to the European Union (EU), which is legally bound to PA implementation, but which differs in its institutional organization and decision-making process. Compared to the EU, Canada's science advisory process is less structured and transparent, scientific advice is not always clearly distinguishable, and policy formulation is not explicit in affording science a responsibility compliant with the PA. The institutional structure and policy framework in Canada has potential to obfuscate the role of science, leading to an erosion of credibility and accountability of fisheries management decisions. We emphasize the strengths of a structured and transparent decision-making process, the existence of a coherent system for categorizing uncertainty with respective rules for decision-making, and unambiguous definitions of the responsibility of science in sustainable fisheries policy.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".