Toward a wild pollinator strategy for Canada: expert-recommended solutions and policy levers
Bibliographic record
Abstract
Invertebrate pollinators are in trouble: particularly documented are declines among bees and butterflies. Interacting stressors include pesticides, pathogens, habitat loss, nonnative species, and climate change. Many governments have strategies to reduce negative pressures on pollinators, but Canada does not despite widespread public interest in pollinator health. This study serves as a needs assessment for science-based policy solutions for wild pollinator conservation in Canada. We designed a Policy Delphi survey technique to identify solutions that experts deem both desirable and feasible. Our secondary aim was to identify research priorities that would inform the implementation of these solutions. Sixty % of the 83 unique solutions were supported and feasible at a high consensus level (10% were “strongly” supported and “definitely” feasible). General themes included improving the Canadian government's approach in assessing pesticide risk to pollinators, curbing pathogen spillover/spillback between managed and wild pollinators, and reducing the reliance of Canadian agricultural systems on pesticides, among others. We discuss solutions in reference to pollinator conservation policies recommended by the broader scientific community and identify policy levers within the context of Canada's highly decentralized approach to biodiversity conservation/management and a political economy that uses high numbers of managed, mostly nonnative bees for pollination services.
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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 | 0.035 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".