Tensions in the communication of science advice on fish and fisheries: northern cod, species at risk, sustainable seafood
Bibliographic record
Abstract
Abstract Providing science-based advice can be challenging. Personal in its reflections, the story that follows asks throughout: What constitutes an appropriate model for the communication of science-based advice that best serves society? The first “front line,” in 1992, involved tenuous hypotheses on the collapse and recovery of Newfoundland's Northern cod (Gadus morhua), raising troubling questions about political influence on science-based advice and on its integrity. These questions subsequently motivated a critique written with two colleagues on the communication of science to decision-makers, provoking a telling invective from a government department in defence of the status quo. The story transitions to my 2000–2012 tenure as a member and then as chair of Canada's national body advising which species should be on the legally binding national at-risk register, illustrating how politically sensitive science-based advice can be objectively, effectively, and independently communicated, unfiltered by vested interests. Since 2009, I have served as independent science advisor on the sourcing of sustainable seafood to Canada's largest food retailer, providing a meaningful, impactful opportunity to advise their decision-makers. Science-based advice, free from political and advocacy-driven vested interests, is a requisite return for tax-supported investments in science. If provision of such advice is a “moral imperative,” as argued more than 60 years ago by C.P. Snow, then scientists are obliged to be the best advisors that we can be.
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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 | 0.031 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.038 | 0.074 |
| Scholarly communication | 0.032 | 0.019 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".