Conservation social science: Understanding and integrating human dimensions to improve conservation
Bibliographic record
Abstract
It has long been claimed that a better understanding of human or social dimensions of environmental issues will improve conservation. The social sciences are one important means through which researchers and practitioners can attain that better understanding. Yet, a lack of awareness of the scope and uncertainty about the purpose of the conservation social sciences impedes the conservation community's effective engagement with the human dimensions. This paper examines the scope and purpose of eighteen subfields of classic, interdisciplinary and applied conservation social sciences and articulates ten distinct contributions that the social sciences can make to understanding and improving conservation. In brief, the conservation social sciences can be valuable to conservation for descriptive, diagnostic, disruptive, reflexive, generative, innovative, or instrumental reasons. This review and supporting materials provides a succinct yet comprehensive reference for conservation scientists and practitioners. We contend that the social sciences can help facilitate conservation policies, actions and outcomes that are more legitimate, salient, robust and effective.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".