Research, public policymaking, and knowledge-translation processes: Canadian efforts to build bridges
Bibliographic record
Abstract
Public policymakers must contend with a particular set of institutional arrangements that govern what can be done to address any given issue, pressure from a variety of interest groups about what they would like to see done to address any given issue, and a range of ideas (including research evidence) about how best to address any given issue. Rarely do processes exist that can get optimally packaged high-quality and high-relevance research evidence into the hands of public policymakers when they most need it, which is often in hours and days, not months and years. In Canada, a variety of efforts have been undertaken to address the factors that have been found to increase the prospects for research use, including the production of systematic reviews that meet the shorter term (but not urgent) needs of public policymakers and encouraging partnerships between researchers and policymakers that allow for their interaction around the tasks of asking and answering relevant questions. Much less progress has been made in making available research evidence to inform the urgent needs of public policymakers and in addressing attitudinal barriers and capacity limitations. In the future, knowledge-translation processes, particularly push efforts and efforts to facilitate user pull, should be undertaken on a sufficiently large scale and with a sufficiently rigorous evaluation so that robust conclusions can be drawn about their effectiveness.
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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 | 0.263 | 0.289 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.024 |
| Science and technology studies | 0.027 | 0.026 |
| Scholarly communication | 0.028 | 0.017 |
| Open science | 0.008 | 0.026 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".