Leading or lagging: How well are climate change considerations being incorporated into Canadian fisheries management?
Bibliographic record
Abstract
In response to fisheries declines and delayed population recoveries, many management agencies globally are integrating alternative strategies that incorporate precautionary and ecosystem considerations, increasingly focusing on climate variability and change. Here, we quantitatively evaluate how these themes have been incorporated into the science and management plans for Canada’s fisheries by analyzing the content of 905 research and management documents published by the Fisheries and Oceans Canada (DFO) for the Atlantic and Eastern Arctic regions. We found that the precautionary approach was mentioned much more frequently (44%) than climate change (11%) or ecosystem approaches to fisheries management (1%). Of research documents that mentioned climate change, 61% contained only a single reference to it, suggesting that it is not quantitatively evaluated in the science that informs the advisory and decision-making processes. Most references to climate change in the DFO research documents expressed high uncertainty of how climate change would impact the stock dynamics. We propose explanations for this and discuss approaches for increasing the incorporation of these themes into Canada’s fishery 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 | 0.025 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.024 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".