A multi-level analysis of links between government institutions and community-based conservation: insights from Iran
Bibliographic record
Abstract
Community-based conservation (CBC) is widely recognized as an important strategy (locally, nationally, and internationally) in efforts to conserve biodiversity. This article examines approaches to improve the effectiveness of CBC by resolving tensions between government institutions and local communities, in terms of respective environmental stewardship activities. These tensions are explored in detail within the context of Iran, and specifically how its conservation and protected area policy and practice interact with its history of indigenous and local conservation. A multi-level analysis considers tensions at the national level but also locally, on Qeshm Island, the largest island in the Persian Gulf. The local-level conservation practices of the island reflect its cultural and stewardship traditions, based on reciprocal links of people and nature. Communities on Qeshm Island utilize a range of collective actions to deal with social, economic, and environmental threats, through a multi-faceted approach that includes maintaining traditional practices, initiating culturally-appropriate economic activity, and engaging in conservation efforts, such as those based on sacred species and sites, on coastal community conservation, and on locally-controlled ecotourism. However, tensions arise between these local activities and government initiatives, especially related to economic development, posing a challenge to local-level stewardship and its mutual benefits for ecosystem health and sustainability of livelihoods. There are various possibilities for overcoming these problems and reinforcing community conservation, including through reinforcement of spatial measures, such as community conserved areas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".