Generation of Priority Research Questions to Inform Conservation Policy and Management at a National Level
Bibliographic record
Abstract
Integrating knowledge from across the natural and social sciences is necessary to effectively address societal tradeoffs between human use of biological diversity and its preservation. Collaborative processes can change the ways decision makers think about scientific evidence, enhance levels of mutual trust and credibility, and advance the conservation policy discourse. Canada has responsibility for a large fraction of some major ecosystems, such as boreal forests, Arctic tundra, wetlands, and temperate and Arctic oceans. Stressors to biological diversity within these ecosystems arise from activities of the country's resource-based economy, as well as external drivers of environmental change. Effective management is complicated by incongruence between ecological and political boundaries and conflicting perspectives on social and economic goals. Many knowledge gaps about stressors and their management might be reduced through targeted, timely research. We identify 40 questions that, if addressed or answered, would advance research that has a high probability of supporting development of effective policies and management strategies for species, ecosystems, and ecological processes in Canada. A total of 396 candidate questions drawn from natural and social science disciplines were contributed by individuals with diverse organizational affiliations. These were collaboratively winnowed to 40 by our team of collaborators. The questions emphasize understanding ecosystems, the effects and mitigation of climate change, coordinating governance and management efforts across multiple jurisdictions, and examining relations between conservation policy and the social and economic well-being of Aboriginal peoples. The questions we identified provide potential links between evidence from the conservation sciences and formulation of policies for conservation and resource management. Our collaborative process of communication and engagement between scientists and decision makers for generating and prioritizing research questions at a national level could be a model for similar efforts beyond Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.098 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".