Pathways to just conservation: A crisp-set qualitative comparative analysis of environmental defender mobilization in conservation conflicts
Bibliographic record
Abstract
Conservation policies intended to address biodiversity loss and climate change are increasingly linked to land dispossession, human rights violations, and the criminalization of environmental defenders. While prior research has highlighted the risks defenders face, less is known about the strategies and conditions that enable them to succeed. This study uses crisp-set Qualitative Comparative Analysis (csQCA) of 25 conservation conflict cases from the Environmental Justice Atlas to identify the pathways through which defenders effectively resist unjust conservation practices. We identify four causal pathways to successful mobilization: two epistemic strategies, where defenders use alternative knowledge mobilization to either strengthen legal claims or build broad coalitions; one preventive strategy focused on early mobilization; and a comprehensive strategy drawing on nearly all conditions, except direct action. Across all pathways, alternative knowledge mobilization, such as defender-led health studies and ecological assessments, plays a central role in successful mobilization, while direct action tactics were notably absent in all successful pathways. These findings challenge assumptions about confrontation as a necessary ingredient for effective resistance and advance new insights into how knowledge politics shape just outcomes in conservation conflicts. As the global conservation community intensifies efforts to safeguard biodiversity and uphold the rights of affected communities, centering the strategies and experiences of environmental defenders is essential to ensuring equitable and effective conservation.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".