Facilitating knowledge transfer between researchers and wildfire practitioners about trust: An international case study
Bibliographic record
Abstract
The importance of knowledge transfer between researchers, policy makers and practitioners is widely recognized. However, barriers to knowledge transfer can make it difficult for practitioners to apply the results of scientific research. This paper describes a project that addressed barriers to knowledge transfer by involving wildfire management practitioners from three countries in developing a trust planning guide. The guide provides information about trust, factors that influence trust and actions that can be taken to build trust in the context of wildfire management. The researchers synthesized academic research into a draft trust planning guide. Wildfire management practitioners and stakeholders provided feedback about the guide and discussed their own experiences in building trust in a workshop setting. The researchers incorporated valuable feedback from the workshops into the final trust planning guide. Benefits and challenges of this process are discussed, and the authors provide recommendations for researchers and funding agencies to facilitate the uptake of research by end-users.
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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 | 0.107 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.011 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".