Untimely publications: Delayed Canadian fisheries science advice limits transparency of decision-making
Bibliographic record
Abstract
The governance framework for wild-capture fisheries varies widely across the world, but most frameworks generate and use science advice in decision-making, developed with some level of peer-review. The Canadian Science Advisory Secretariat (CSAS) oversees the peer-review process of science related to the management of Canada’s fisheries and oceans; the resultant publications with science advice are the main source of scientific evidence used in decision-making in Canada’s fisheries. The CSAS has a publicly available policy intended to ensure transparency and timely dissemination of publications that is currently under review. To examine the effectiveness of the existing policy, inform its revision and development of similar policies in other jurisdictions, this paper evaluates the timeliness of the publication of scientific information to support the management of Canada’s fisheries and oceans from recent CSAS meetings against current policy deadlines. It was found that from 2017 to 2019, implementation of the policy was poor, with most documents published late, if at all. Furthermore, the science advice considered most during fisheries management decision-making was often not publicly available until after the decision was made and communicated. Despite having a policy to promote transparency, public engagement in policy and decision-making is being limited and a common understanding of the science evidence underlying advice inhibited. Transparency, which is becoming a cornerstone principle in modern fisheries management, is being compromised in Canadian fisheries management.
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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 | 0.158 | 0.590 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.039 | 0.016 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".