Co-management and Indigenous Communities: Barriers and Bridges to Decentralized Resource Management: Introduction
Bibliographic record
Abstract
Analyzing Co-management I n recent decades, there has been a profusion of new decentralized institutions for resource management.They have developed as a result of the efforts made by state managers and local resource users to address an array of crises, conflicts and dilemmas surrounding common property resources.Through processes that are variously described as "co-management" or "co-operative management" or "community-based management" managers at the state level and users at the local level have together created scores of new decentralized common property institutions.As joint ventures, these institutions combine different aspects of both state-level and community-level approaches to governance.Accompanying this growth in common property institutions are efforts to analyze them and, as a consequence, the literature on co-management is also growing.Analyses of co-management are becoming quite diverse as a variety of approaches have been adopted, and a complex mix of differing and sometimes conflicting research findings is emerging.This special theme issue of Anthropologica seeks to explore this diversity and to highlight a set of themes and questions related to co-management.It also seeks to highlight research on relationships between indigenous communities and nation states.The authors in this issue adopt a variety of analytical approaches, some more than one, and collectively the papers address issues raised by political ecology, forms of control deployed by modern nation states, critical approaches to issues of empowerment and Indigenous visions of relations to the state.The findings that these papers present do not fit neatly together, nor do they implicitly fit within any one of the theoretical frameworks being used, but they do pose basic questions and tackle issues of wide import that are emerging from this rapidly developing area of research.In the process they also challenge some earlier approaches and assumptions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".