An optimistic outlook on the use of evidence syntheses to inform environmental decision‐making
Bibliographic record
Abstract
Abstract Practitioners and policymakers working in environmental arenas make decisions that can have large impacts on ecosystems. Basing such decisions on high‐quality evidence about the effectiveness of different interventions can often maximize the success of policy and management. Accordingly, it is vital to understand how environmental professionals working at the science‐policy interface view and use different types of evidence, including evidence syntheses that collate and summarize available knowledge on a specific topic to save time for decision‐makers. We interviewed 84 senior environmental professionals in Canada working at the science‐policy interface to explore their confidence in, and use of, evidence syntheses within their organizations. Interviewees value evidence syntheses because they increase confidence in decision‐making, particularly for high‐profile or risky decisions. Despite this enthusiasm, the apparent lack of available syntheses for many environmental issues means that use can be limited and tends to be opportunistic. Our research suggests that if relevant, high quality evidence syntheses exist, they are likely to be used and embraced in decision‐making spheres. Therefore, efforts to increase capacity for conducting evidence syntheses within government agencies and/or funding such activities by external bodies have the potential to enable evidence‐based decision‐making.
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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 | 0.764 | 0.812 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.023 | 0.012 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.042 | 0.043 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.023 | 0.032 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".