Incentivizing stewardship in a biodiversity hot spot: land managers in the grasslands
Bibliographic record
Abstract
Federal and provincial governments of Canada recently signed onto a Pan-Canadian Approach to Transforming Species at Risk Conservation. The approach is based on collaboration among jurisdictions and stakeholders to enhance multiple species and ecosystem-based conservation in selected biodiversity hot spots. In this review paper, we focus on one of the biodiversity hot spots—the South of the Divide area in the province of Saskatchewan—to propose appropriate mechanisms to incentivize stewardship on agricultural Crown lands. Through a focused review and synthesis of empirical studies, we propose a range of policy instruments and incentives that can help deliver multi-species at risk conservation on Crown agricultural lands in Saskatchewan. We outline a range of policy instruments and incentives that are relevant to conservation on Crown agricultural lands and argue that a portfolio of options will have the greatest social acceptability. More germane is the need to foster collaboration between the government of Saskatchewan, other provincial/territorial governments, and the federal government, nongovernmental organizations, and land managers. Such collaboration is critical for enhanced decision-making and institutional change that reflects the urgent call for creating awareness of species at risk policies, building trust, and leveraging the local knowledge of land managers for conservation.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".