Overcoming Common Anxieties in Knowledge Translation: Advice for Scholarly Issue Advocates
Bibliographic record
Abstract
Policy Points Faced with urgent threats to human health and well-being such as climate change, calls among the academic community are getting louder to contribute more effectively to the implementation of the evidence generated by our research into public policy. As interest in knowledge translation (KT) surges, so have a number of anxieties about the field's shortcomings. Our paper is motivated by a call in the literature to render useful advice for those beginning in KT on how to advance impact at a policy level. By integrating knowledge from fields such as political science, moral psychology, and marketing, we suggest that thinking and acting like marketers, lobbyists, movements, and political scientists would help us advance on the quest to bridge the chasm between evidence and policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.266 | 0.533 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.021 | 0.060 |
| Scholarly communication | 0.046 | 0.083 |
| Open science | 0.011 | 0.030 |
| Research integrity | 0.076 | 0.071 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".