Embracing Disruptive New Science? Biotelemetry Meets Co-Management in Canada's Fraser River
Bibliographic record
Abstract
Abstract Evidence-based management of fisheries means being continually open to new sources of scientific findings and data, but this is difficult when there is uncertainty or disagreement about their value and utility. We submit that this is the case for rapidly advancing animal tracking research, or biotelemetry. While biotelemetry science has been broadly accepted in fisheries and aquatic research communities, its incorporation into fisheries policy and management has been limited. To gain insight into this disjuncture, we conducted an exploratory study of perspectives on biotelemetry among government employees and nongovernmental stakeholders involved in co-managing salmon fisheries in Canada's Fraser River. Using a knowledge mobilization theoretical framework, we examine how respondents perceived biotelemetry research across three dimensions: its epistemic value (its capacity to generate useful and valid new knowledge), its practical value (relative to real-world considerations such as cost), and its degree of fit or discord with existing policy and management practices. We find a wide range of views between both groups, which may explain the hesitant uptake of biotelemetry into policy and management in this case. We conclude by advancing several research questions as a guide for future study of the integration of new sources of knowledge into evidence-based 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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".