Caught in the middle - Inaction and overlap in governance and decision-making for Canada’s imperiled wild steelhead
Bibliographic record
Abstract
Biodiversity loss is one of the most serious challenges facing humanity and planetary well-being. Even for iconic species of great cultural and symbolic value, we are largely failing to preserve them and the habitats upon which they depend. This article analyzes one such troubling case, the precipitous decline of wild steelhead ( Oncorhynchus mykiss ) populations in British Columbia’s Thompson River. These populations are declining despite high levels of public attention and interventions from both provincial and federal governments. This raises important questions about how such losses could happen despite intense scrutiny and high motivation for action. Our analysis of this case study is based on a review of policy documents and interviews with steelhead anglers (42) and fisheries managers (5) in the Thompson River region. Our analysis revealed that Thompson steelhead are ‘caught in the middle’ of competing government priorities, jurisdictional uncertainties and overlap, and recalcitrant rightsholder and stakeholder conflicts. The result is policy and decision-making paralysis that has entrenched the decline. We submit that there are lessons to be learned from this case for biodiversity management in Canada and elsewhere that involve deep and urgent reforms to environmental governance.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.019 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".