Supporting actionable science for environmental policy: Advice for funding agencies from decision makers
Bibliographic record
Abstract
Successful incorporation of scientific knowledge into environmental policy and decisions is a significant challenge. Although studies on how to bridge the knowledge-action gap have grown rapidly over the last decade, few have investigated the roles, responsibilities, and opportunities for funding bodies to meet this challenge. In this study we present a set of criteria gleaned from interviews with experts across Canada that can be used by funding bodies to evaluate the potential for proposed research to produce actionable knowledge for environmental policy and practice. We also provide recommendations for how funding bodies can design funding calls and foster the skills required to bridge the knowledge-action gap. We interviewed 84 individuals with extensive experience as knowledge users at the science-policy interface who work for environmentally focused federal and provincial/territorial government bodies and non-governmental organizations. Respondents were asked to describe elements of research proposals that indicate that the resulting research is likely to be useful in a policy context, and what advice they would give to funding bodies to increase the potential impact of sponsored research. Twenty-five individuals also completed a closed-ended survey that followed up on these questions. Research proposals that demonstrated 1) a team with diverse expertise and experience in co-production, 2) a flexible research plan that aligns timelines and spatial scale with policy needs, 3) a clear and demonstrable link to a policy issue, and 4) a detailed and diverse knowledge exchange plan for reaching relevant stakeholders were seen as more promising for producing actionable knowledge. Suggested changes to funding models to enhance utility of funded research included 1) using diverse expertise to adjudicate awards, 2) supporting co-production and interdisciplinary research through longer grant durations and integrated reward structures, and 3) following-up on and rewarding knowledge exchange by conducting impact evaluation. The set of recommendations presented here can guide both funding agencies and research teams who wish to change how applied environmental science is conducted and improve its connection to policy and practice.
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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 | 0.143 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.023 | 0.016 |
| Scholarly communication | 0.027 | 0.021 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.021 | 0.016 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".