An optimistic outlook on the use of evidence syntheses to inform environmental decision‐making
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it