What is “usable” knowledge? Perceived barriers for integrating new knowledge into management of an iconic Canadian fishery
Bibliographic record
Abstract
Understanding the perspectives of knowledge users and the demands of their decision-making environment would benefit researchers looking to enhance the utility of the knowledge they generate. Using the Fraser River Pacific salmon fishery as a case study, we investigate the views of 49 government employees and stakeholders regarding the barriers to incorporating new knowledge into fisheries management. Our study uses analysis of qualitative data structured by a knowledge–action framework, which revealed that 90% of respondents perceived the contextual dimension (e.g., institutional structures and norms) as a barrier for incorporating new knowledge, followed by barriers related to the characteristics of knowledge actors (52% of respondents), characteristics of the knowledge (27%), time and timing (27%), knowledge transfer strategies (17%), and relational dimension (8%). The identified barriers have indirect–direct relationship with knowledge producers and appear hierarchical in nature. We note that informal relationships can enable conditions whereby knowledge users can access new knowledge, and knowledge producers can gain insights on users’ needs. We discuss lessons learned from the case, which we believe can be applied more beyond fisheries.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".