Community participation in protected areas in Iran, Afghanistan, and India
Bibliographic record
Abstract
Community participation has become an essential part of protected area (PA) management worldwide. This thesis contributes suggestions for improving conservation effectiveness and efficiency by boosting responsible local community participation in PA management. I studied Sabzkouh PA in Iran, Shah Foladi PA in Afghanistan, and Bhitarkanika National Park and Wildlife Sanctuary in India to examine: (1) what factors affect community participation in PAs in developing countries? (2) what roles can state governments and Non-Governmental Organizations (NGOs) play to support community participation? (3) how can application of equity criteria improve PA management? My studies, conducted between 2009 and 2018, combined document reviews with personal observations, participatory rural appraisal workshops, and open-ended interviews with local community members, state government staff, NGO representatives, and researchers. The result is a suite of recommendations and cautions for conservation practitioners seeking to improve PA management through collaborations with local communities. Respecting local communities’ knowledge, norms, and livelihoods surfaced as important components for building relationships and trust between the local communities and the state governments. Building trust and capacities is contingent on satisfying essential community needs and on transparent, fair, and collaborative PA management planning and implementation. Community based natural resources management projects can share the benefits and reduce the burdens of conservation for the communities while building the capacity of local communities to participate in PA management. Senses of equity and justice arise from deliberate collaboration and information sharing between the state government and local communities. Promoting shared governance, including the use of multi-stakeholder management committees, is an apt tool for decision-making that represents the full range of local community constituents, interests, and preferences. National and international NGOs can facilitate relationships between the state and local communities, provide funding, and fill gaps in management and technical capacities. Community participation in PA management and governance is a process that requires ongoing dialogue and trust among the stakeholders.
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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 | 0.002 | 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 | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".