Assessing diverse evidence to improve conservation decision‐making
Bibliographic record
Abstract
Abstract Meeting the urgent need to protect and restore ecosystems requires effective decision‐making through wisely considering a range of evidence. However, weighing and assessing evidence to make complex decisions is challenging, particularly when evidence is of diverse types, subjects, and sources, and varies greatly in its quality and relevance. To tackle these challenges, we present the Balance Evidence Assessment Method (BEAM), an intuitive way to weigh and assess the evidence relating to the core assumptions underpinning the planning and implementation of conservation projects, strategies, and actions. Our method directly tackles the question of how to bring together diverse evidence whilst assessing its relevance, reliability, and strength of support for a given assumption, which can be mapped, for example to a Theory of Change. We consider how simple principles and safeguards in applying this method could help to respectfully, and equitably, include more local forms of knowledge when assessing assumptions, such as by ensuring diverse groups of individuals contribute and assess evidence. The method can be flexibly applied within existing decision‐making tools, platforms, and frameworks whenever assumptions (i.e., claims and hypotheses) are made. This method could greatly facilitate and improve the weighing of diverse evidence to make decisions in a range of situations, from local projects to global policy platforms.
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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 | 0.514 | 0.703 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.052 | 0.017 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.030 | 0.027 |
| Open science | 0.007 | 0.028 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.017 | 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".