Revisiting the challenge: perspectives on Canada’s freshwater fisheries policies three decades after the Pearse Report
Bibliographic record
Abstract
A seminal report by Peter H. Pearse (1988; Rising to the Challenge: A New Policy for Canada’s Freshwater Fisheries, Canadian Wildlife Federation, Ottawa) outlined 62 policy recommendations focused on the management of Canada’s inland fisheries. Over three decades later, freshwater ecosystems and inland fisheries in Canada are still facing similar challenges with many emerging ones that could not have been foreseen. Here, we reflect on the contemporary relevance of the Pearse Report and propose recommendations that policy makers should consider. Broadly, our recommendations are: (1) manage fishes, fisheries, and habitat using a holistic co-management framework, with clearly defined fishery jurisdictions and partnerships with Indigenous governments; (2) engage in transparent, inclusive, and agile research to support decision-making; (3) facilitate knowledge co-production, involving interdisciplinary projects with diverse groups of actors and sectors including Indigenous Peoples, anglers, policy makers, scientists/researchers, governments, and the public; (4) embrace technological advances to support freshwater fisheries stock assessment and management; and (5) align policy and management activities in Canada with global initiatives related to increasing the sustainability of inland fisheries. We advocate for an updated comprehensive report such as the Pearse Report to ensure that we embrace robust, inclusive, and sustainable management strategies and policies for Canada’s inland fisheries for the next 30 years. It is time to again rise to the challenge.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.033 | 0.017 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.015 | 0.018 |
| 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".