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Record W4409075222 · doi:10.1111/cobi.14453

Advancing social impact assessments for more effective and equitable conservation

2025· review· en· W4409075222 on OpenAlexaboutno aff
Neil Dawson, Helen Suich

Bibliographic record

VenueConservation Biology · 2025
Typereview
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousSocial impact assessmentTraditional knowledgePolitical sciencePublic relationsEnvironmental planningEnvironmental resource managementSociologyGeographyEcology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.438
Teacher spread0.403 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2025
Admission routes1
Has abstractyes

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