Applying a knowledge–action framework for navigating barriers to incorporating telemetry science into fisheries management and conservation: a qualitative study
Bibliographic record
Abstract
Telemetry studies have produced fundamental knowledge on animal biology and ecology that has the potential to improve management of aquatic resources such as fisheries. However, the use and integration of telemetry-derived knowledge into practice remain tenuous, so we surveyed 212 fish telemetry experts to understand existing barriers for incorporating telemetry-derived knowledge into fisheries management practices. We apply a sociological knowledge–action framework to structure the findings, which revealed four primary challenges to integrating telemetry findings into management: (1) the perceived uncertainties and unclear relevance of telemetry findings; (2) the underlying motivations and constrained rationalities of actors that can lead to inaction or suboptimal decisions; (3) the constraints of institutions, governance structures, and lack of organizational support, and (4) time and mismatches in scale, culture, and world views. On a more positive note, the relational dimension (collaboration, trust, and relationship building) appears to be important for overcoming and avoiding barriers. We further provide recommendations to navigate these perceived barriers and argue that these lessons also apply to other fields of applied ecology, conservation, and resource 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 | 0.079 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".