Is scientific inquiry still incompatible with government information control? A quarter-century later
Bibliographic record
Abstract
Twenty-six years ago, in response to regionally devastating fisheries collapses in Canada, Hutchings et al. asked “Is scientific inquiry incompatible with government information control?” Now, a quarter-century later, we review how government science advice continues to be influenced by non-science interests, particularly those with a financial stake in the outcome of the advice. We use the example of salmon aquaculture in British Columbia, Canada, to demonstrate how science advice from Fisheries and Oceans Canada (DFO) can fail to be impartial, evidence-based, transparent, and independently reviewed—four widely implemented standards of robust science advice. Consequently, DFO's policies are not always supported by the best available science. These observations are particularly important in the context of DFO having struggled to sustainably manage Canada's marine resources, creating socio-economic uncertainty and putting the country's international reputation at risk as it lags behind its peers. We conclude by reiterating Hutchings et al.’s unheeded recommendation for a truly independent fisheries-science advisory body in Canada to be enshrined in the decision-making process.
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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 | 0.093 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.014 | 0.059 |
| Scholarly communication | 0.038 | 0.022 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.019 | 0.032 |
| Insufficient payload (model declined to judge) | 0.005 | 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".