Advancing Evidence-Based Decision-Making in Large Landscape Conservation Through the Social Sciences: A Research Agenda for the Yellowstone to Yukon Region
Bibliographic record
Abstract
As the world's mountains are significant hotspots of biodiversity and home to hundreds of millions of people, they are ideal locations in which to investigate and develop the conservation social sciences in a systematic way to help inform conservation decision-making and policy. Here, we discuss the development of a social science research agenda for the Yellowstone to Yukon Conservation Initiative, a transboundary environmental organization working in Canada and the United States. We suggest that this process is useful for others to undertake in similar conservation landscapes and mountain systems as we strive to better understand how people live in, play in, benefit from, and visit the globe's mountain regions. We outline an agenda for collaborative social science research in the Yellowstone to Yukon region related to 4 themes and offer 12 priority questions as launching points for interested researchers to explore in more detail. Through a review of relevant literature on the 4 themes, we identify research gaps that, if addressed, could usefully inform decision-making across the Yellowstone to Yukon region. Finally, we call on the research community to focus its curiosity and resources on answering these questions and encourage funders and institutions to support them in doing so.
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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 | 0.233 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.014 | 0.014 |
| 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".