Free Fallin’? The decline in evidence-based decision-making by Canada’s protected areas managers
Bibliographic record
Abstract
The conservation of biodiversity requires various forms of evidence to ensure effective outcomes. In this study, we provide an updated assessment of the state of evidence-based decision-making in Canada’s protected areas organizations by examining practitioner perceptions of: ( i) the value and use of various forms of evidence, ( ii) the availability of evidence to support decisions, and ( iii) the extent to which various institutional and behavioural barriers influence the use of evidence. Our results compare national surveys conducted in 2019 and 2013, revealing a significant and concerning decline in the use of all forms of evidence. We found significant declines in the use of peer-reviewed literature, local knowledge, and Indigenous knowledge. Our results correspondingly demonstrate a host of systemic barriers to the effective use of evidence, including a lack of trust, how to deal with uncertainty, and limited training. These challenges persist at a time when the quantity of information is greater than ever, and recognition of the value of Indigenous knowledge is relatively high (and increasing). Leadership is required to cultivate more relevant evidence, to embed scientists and Indigenous Knowledge-Holders in conservation organizations, to (re)establishing knowledge sharing forums, and to establish accountability and reporting measures to support efforts aimed at effectively achieving Canada’s biodiversity conservation goals.
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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 | 0.043 | 0.128 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".