Environmental evidence in action: on the science and practice of evidence synthesis and evidence-based decision-making
Bibliographic record
Abstract
In civil society we expect that policy and management decisions will be made using the best available evidence. Yet, it is widely known that there are many barriers that limit the extent to which that occurs. One way to overcome these barriers is via robust, comprehensive, transparent and repeatable evidence syntheses (such as systematic reviews) that attempt to minimize various forms of bias to present a summary of existing knowledge for decision-making purposes. Relative to other disciplines (e.g., health care, education), such evidence-based decision-making remains relatively nascent for environment management despite major threats to humanity, such as the climate, pollution and biodiversity crises demonstrating that human well-being is inextricably linked to the biophysical environment. Fortunately, there are a growing number of environmental evidence syntheses being produced that can be used by decision makers. It is therefore an opportune time to reflect on the science and practice of evidence-based decision-making in environment management to understand the extent to which evidence syntheses are embraced and applied in practice. Here we outline a number of key questions related to the use of environmental evidence that need to be explored in an effort to enhance evidence-based decision-making. There is an urgent need for research involving methods from social science, behavioural sciences, and public policy to understand the basis for patterns and trends in environmental evidence use (or misuse or ignorance). There is also a need for those who commission and produce evidence syntheses, as well as the end users of these syntheses to reflect on their experiences and share them with the broader evidence-based practice community to identify needs and opportunities for advancing the entire process of evidence-based practice. It is our hope that the ideas shared here will serve as a roadmap for additional scholarship that will collectively enhance evidence-based decision-making and ultimately benefit the environment and humanity.
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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 | 0.684 | 0.773 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.021 | 0.008 |
| Bibliometrics | 0.033 | 0.028 |
| Science and technology studies | 0.008 | 0.094 |
| Scholarly communication | 0.053 | 0.059 |
| Open science | 0.015 | 0.034 |
| Research integrity | 0.040 | 0.048 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".