The Social Fit of Conservation Policy on Working Landscapes
Bibliographic record
Abstract
The working landscapes approach is valuable for extending conservation beyond the boundaries of strict protected areas. Conservation on working landscapes relies heavily on social acceptance and the alignment of conservation programs with local livelihoods. This paper examines farmers and ranchers’ preferences for different policy instruments and incentives that form programs for endangered species conservation in Canada's temperate grassland ecosystem—one of the most imperiled ecosystems on earth. Generally, farmers and ranchers are more concerned about the restrictions that programs impose than they are about the amount of funding the programs provide. Although, trust in the program delivery agent is also a key consideration. Overall, farmers and ranchers prefer instruments that maintain their property rights and provide continuous financial incentives. Additionally, they prefer shorter-term contracts to longer-term contracts or agreements in perpetuity. Many of their preferences extend beyond status quo conservation in Canada, which relies heavily on restrictions, non-continuous financial incentives (i.e., one-time payments), and long-term agreements. We need to augment the existing suite of programs to include flexible and adaptive options to maintain, improve and protect grasslands and the species that depend on them.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".