Addressing conflict through collective action in natural resource management
Bibliographic record
Abstract
The food security crisis and international “land grabs” have drawn renewed attention to the role of natural resource competition in the livelihoods of the rural poor. While significant empirical research has focused on diagnosing the links between natural resource competition and (violent) conflict, much less has focused on the dynamics of whether and how resource competition can be transformed to strengthen social-ecological resilience and mitigate conflict. Focusing on this latter theme, this review synthesizes evidence from cases in Africa, Asia, and Latin America. Building on an analytical framework designed to enable such comparative analysis, we present several propositions about the dynamics of conflict and collective action in natural resource management, and a series of recommendations for action. These propositions are: that collective action in natural resource management is influenced by the social-ecological and governance context, that natural resource management institutions affect the incentives for conflict or cooperation, and that the outcomes of these interactions influence future conflict risk, livelihoods, and resource sustainability. Action recommendations concern policies addressing resource tenure, conflict resolution mechanisms, and social inequalities, as well as strategies to strengthen collective action institutions in the natural resource sectors and to enable more equitable engagement by marginalized groups in dialogue and negotiation over resource access and use.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".