The Closer, the Better? Untangling Scientist–Practitioner Engagement, Interaction, and Knowledge Use
Bibliographic record
Abstract
Abstract Scholarship on climate information use has focused significantly on engagement with practitioners as a means to enhance knowledge use. In principle, working with practitioners to incorporate their knowledge and priorities into the research process should improve information uptake by enhancing accessibility and improving users’ perceptions of how well information meets their decision needs, including knowledge credibility, understandability, and fit. Such interactive approaches, however, can entail high costs for participants, especially in terms of financial, human, and time resources. Given the likely need to scale up engagement as demand for climate information increases, it is important to examine whether and to what extent personal interaction is always a necessary condition for increasing information use. In this article, we report the results from two experimental studies using students as subjects to assess how three types of interaction (in-person meeting, live webinar, and self-guided instruction) affect different aspects of climate information usability. Our findings show that while in-person interaction is effective in enhancing understanding of climate knowledge, in-person interaction may not always be necessary, depending on the kinds of information involved and outcomes desired.
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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 | 0.026 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".