Considering people in systematic conservation planning: insights from land system science
Bibliographic record
Abstract
Species and ecosystems worldwide continue to decline and disappear in spite of decades of investment in conservation efforts. Systematic conservation planning ( SCP ) is a field of study designed to improve conservation programs by identifying land configurations that, if protected, would most efficiently sustain biodiversity. Despite contributing to species persistence in landscapes, SCP has been criticized for replacing site‐based conservation plans that often consider social context. In contrast, land system science ( LSS ), an emerging field that explores the process of land‐use and land‐cover change, integrates social systems and processes into conservation analyses. We suggest that by incorporating insights from LSS on social processes (eg livelihood adaptation or agricultural intensification), SCP can enhance the legitimacy of conservation plans, thereby reducing the gap between conservation planning and implementation. This represents a necessary first step for SCP to reinvent itself as a decision‐support tool that helps to reconcile the long‐standing divide between landscape‐level species conservation and social needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".