Deliberative Dialogues Between Policy Makers and Researchers in Canada and Australia
Bibliographic record
Abstract
Knowledge translation (KT) and implementation science are growing fields in Canada, Australia, and worldwide. Many audiences are targeted as KT knowledge users—policy makers represent one key knowledge user in the health care field. The need for policy makers to understand research and for researchers to understand policy processes is commonly recognized. There is also increasing interest in health policy that focuses on KT as a framework for understanding the use of evidence and, in particular, describing the influence of research on policy along with concepts of coproduction and user involvement. With relationship building central to successful evidence-informed policy, this article explores deliberative dialogue as a potential approach to enhancing KT. It describes two examples of researcher efforts to cultivate relationships and contacts with policy and decision makers via such dialogues and illustrates the inherent opportunities and challenges of doing so.
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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 | 0.101 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.089 | 0.046 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.012 | 0.018 |
| 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".