A social–ecological approach to conservation planning: embedding social considerations
Why this work is in the frame
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Bibliographic record
Abstract
Many conservation plans remain unimplemented, in part because of insufficient consideration of the social processes that influence conservation decisions. Complementing social considerations with an integrated understanding of the ecology of a region can result in a more complete conservation approach. We suggest that linking conservation planning to a social–ecological systems (SES) framework can lead to a more thorough understanding of human–environment interactions and more effective integration of social considerations. By characterizing SES as a set of subsystems, and their interactions with each other and with external factors, the SES framework can improve our understanding of the linkages between social and ecological influences on the environment. Using this framework can help to identify socially and ecologically focused conservation actions that will benefit ecosystems and human communities, and assist in the development of more consistent evidence for evaluating conservation actions by comparing conservation case studies.
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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 | 0.001 | 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 it