Standards of conduct and reporting in evidence syntheses that could inform environmental policy and management decisions
Bibliographic record
Abstract
Accurate, unbiased and concise synthesis of available evidence following clear methodology and transparent reporting is necessary to support effective environmental policy and management decisions. Without this, less reliable and/or less objective reviews of evidence could inform decision making, leading to ineffective, resource wasteful interventions with potential for unintended consequences. We evaluated the reliability of over 1000 evidence syntheses (reviews and overviews) published between 2018 and 2020 that provide evidence on the impacts of human activities or effectiveness of interventions relevant to environmental management. The syntheses are drawn from the Collaboration for Environmental Evidence Database of Evidence Reviews (CEEDER), an online, freely available evidence service for evidence users that assesses the reliability of evidence syntheses using a series of published criteria. We found that the majority of syntheses have problems with transparency, replicability and potential for bias. Overall, our results suggest that most recently published evidence syntheses are of low reliability to inform decision making. Reviews that followed guidance and reporting standards for evidence synthesis had improved assessment ratings, but there remains substantial variation in the standard of reviews amongst even these. Furthermore, the term 'systematic review', which implies conformity with a methodological standard, was frequently misused. A major objective of the CEEDER project is to improve the reliability of the global body of environmental evidence reviews. To this end we outline freely available online resources to help improve review conduct and reporting. We call on authors, editors and peer reviewers to use these resources to ensure more reliable syntheses in the future.
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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 | 0.822 | 0.944 |
| Meta-epidemiology (narrow) | 0.007 | 0.012 |
| Meta-epidemiology (broad) | 0.021 | 0.016 |
| Bibliometrics | 0.078 | 0.061 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.047 | 0.034 |
| Open science | 0.014 | 0.030 |
| Research integrity | 0.025 | 0.031 |
| Insufficient payload (model declined to judge) | 0.027 | 0.029 |
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".