Advancing social impact assessments for more effective and equitable conservation
Bibliographic record
Abstract
Social objectives for conservation have expanded beyond consideration of material costs and benefits to recognize Indigenous Peoples' and local communities' rights, the importance of their full and effective participation, and the contribution of customary institutions and plural knowledge systems. Social impact assessment can help conservation professionals understand how social principles are reflected in practice and inform governance improvements. We reviewed the peer-reviewed and gray literature describing methodological approaches and their application to social impact assessments in conservation. We investigated whether the methodologies used empirically are advancing to reflect contemporary social objectives, in particular around rights, procedural justice, and recognition of identities and knowledge. In our initial review of methodological papers, we identified two interrelated themes that can drive high-quality social impact assessment: incorporation of the perspectives, knowledge systems and participation of Indigenous Peoples and local communities, and the completeness and appropriateness of methodological approaches adopted. We categorized these themes into principles of good practice (e.g., local participation and disaggregated social analyses) and used them to analyze empirical social impact assessments and explore the extent to which they were applied. Empirical studies tended not to reflect expanded social objectives or methodological advancements. Few studies covered multiple domains of social impact, disaggregated results by social group, involved Indigenous Peoples and local communities, or presented a clear and informed methodological approach and strategy for use of mixed methods. To improve the quality of social impact assessments commensurate with the needs and social standards associated with conservation in the time of the Kunming-Montreal Global Biodiversity Framework, the equitable involvement of Indigenous Peoples and local communities in any assessment; the establishment of clear, appropriate, and complete methodological approaches; and the integration of social impact assessments into governance processes are essential.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".