The practice and promise of private land conservation
Bibliographic record
Abstract
In many countries around the globe, private freehold lands cover large areas.Conservation on these private lands, next to statutory protected areas, promises to play a critical role in efforts for reaching internationally agreed environmental protection targets.Lying at the heart of an emerging land system science, in which ecology, economics, geography, psychology, and other social sciences interact, private land conservation is reflecting the intertwined and multiscalar processes of our rapidly transforming world.Situated at this disciplinary meeting point, private land conservation invites a great breadth of approaches and cross-disciplinary work that offer deep insights into social and environmental change, often from surprising angles.Although many questions remain in private land conservation, we can now build on a large body of recent high-quality studies as we push this field forward in both research and practice.The Special Feature "Private Land Conservation -Landowner Motives, Policies, and Outcomes of Conservation Measures in Unprotected Landscapes" brings together contributions that explore the diversity of recent advances in private land conservation science.As an introduction to this Special Feature, first we are reviewing recent dynamics in important social-ecological drivers with bearing on private land conservation science.We go on to introduce the individual contributions to this Special Feature and then examine common themes as they are emerging from these papers, including the need for flexibility in conservation approaches, pursuit of community cobenefits of conservation, increasing consideration of environmental justice questions, and acknowledgment of the importance of social psychology in shaping private land conservation.We conclude with identification of knowledge gaps and recommendations for future research, as we advance from diagnostics to normative work in private land conservation science.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.039 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".