A practical approach to assessing existing evidence for specific conservation strategies
Bibliographic record
Abstract
Abstract There is currently a great deal of work being undertaken to collect, analyze, and synthesize available evidence about the effectiveness of conservation strategies. But substantial challenges still remain in enabling practitioners to assess and apply this evidence to their conservation work in an efficient manner. To solve these challenges, there is growing recognition of the need to use situation assessments and theory of change pathways to detail a set of analytical questions and specific assumptions that can be assessed against the evidence base to “make the case” for a proposed strategy and to identify gaps in knowledge. In this study, we first provide updated definitions of some key terms. We then present and provide examples of an approach to enable practitioners to evaluate the evidence base for the critical assumptions that underlie their specific conservation strategies and to wisely use evidence coming from different knowledge systems. This practical approach, which was developed through a series of pilot tests with Parks Canada projects, involves four iterative steps: (1) identify critical questions and assumptions requiring evidence; (2) assemble and assess the specific and generic evidence for each assumption; (3) determine confidence in evidence and its implications; and (4) validate the assessment and iteratively adapt as needed. Ideally, this approach can be integrated into existing decision‐making frameworks and can also facilitate better cooperation between researchers who synthesize evidence and practitioners who use evidence to make conservation both more effective and efficient.
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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 | 0.453 | 0.589 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.043 | 0.016 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.029 | 0.026 |
| Open science | 0.012 | 0.026 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".