Evidence-based decision-making in Canada’s protected areas organizations: Implications for management effectiveness
Bibliographic record
Abstract
Aichi Biodiversity Target 19 calls on Parties to the United Nations Convention on Biological Diversity (CBD) to improve, share, transfer, and apply knowledge. In this study, we provide an initial assessment of the state of evidence-based decision-making in Canada’s protected areas organizations by examining (1) the value and use of various forms of evidence by managers and (2) the extent to which institutional conditions enable or inhibit the use of evidence in decision-making. Results revealed that although managers value and use many forms of evidence in their decision-making, information produced by staff and their organizations are given priority. Other forms of evidence, such as Indigenous knowledge and peer-reviewed information, are valued and used less. The most significant barriers to evidence-based decision-making were limited financial resources, lack of staff, inadequate timeframes for decision-making, a lack of monitoring programs, and a disconnect between researchers and decision-makers. Overall, our results suggest that the potential benefits of evidence-based approaches are not being maximized in Canada’s protected areas organizations. We propose several recommendations to introduce or improve the use of diverse forms of evidence to enhance management effectiveness of Canada’s protected areas and by extension conservation outcomes.
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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 | 0.223 | 0.452 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.026 | 0.008 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".