A mixed methodology for evaluating use of evidence in conservation planning
Bibliographic record
Abstract
Conservation practitioners widely recognize the importance of making decisions based on the best available evidence. However, the effectiveness of evidence use in conservation planning is rarely assessed, which limits opportunities to improve evidence-based practice. We devised a mixed methodology for empirically evaluating use of evidence that applies social science tools to systematically appraise what kinds of evidence are used in conservation planning, to what effect, and under what limitations. We applied our approach in a case study of the Nature Conservancy of Canada (NCC), a leading land conservation organization. We conducted qualitative and quantitative analyses of 65 NCC planning documents (n = 13 in-depth) to identify patterns in evidence use, and surveyed 35 conservation planners to examine experiences of and barriers to using evidence. Although claims in plans contained a wide range of evidence types, 26% of claims were not referenced or associated with an identifiable source. Lack of evidence use was particularly apparent in claims associated with direct threats, particularly those identified as low (71% coded as insufficient or lacking evidence) or medium (45%) threats. Survey participants described relying heavily on practitioner experience and highlighted capacity limitations and disciplinary gaps in expertise among planning teams as barriers to using evidence effectively. We found that although time-intensive, this approach yielded actionable recommendations for improving evidence use in NCC conservation plans. Similar mixed-method assessments may streamline the process by including interviews and refining the document analysis frames to target issues or sections of concern. We suggest our method provides an accessible and robust point of departure for conservation practitioners to evaluate whether the use of conservation planning reflects in-house standards and more broadly recognized best practices.
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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 | 0.527 | 0.618 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.059 | 0.035 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 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".