Developing co-management for conservation and local development in China’s national parks: findings from focus group discussions in the Sanjiangyuan Region
Bibliographic record
Abstract
Environmental protection in China has progressed significantly in the past decades, including introduction of more collaborative approaches in the management of protected areas and the establishment of a new national park system, and many milestones have been achieved. While such developments are driven largely by national and global goals, the people who are most affected are those who reside in the protected landscapes. A range of strategies have been proposed and tried in relation to local development, with many important lessons learned, yet little has been heard to date directly from the community stakeholders themselves. In this study we report on feedback and recommendations received from focus group discussions in vicinity of China’s first national park, Sanjiangyuan, regarding lived experiences of “community co-management” by Tibetan herders and local officials. Overall, the most recent National Park model is deemed successful, albeit with some notable perceived limitations. Focus group discussions' participants recommend more balanced compensation opportunities including for communities living outside but in close proximity to the park, eased restrictions on ecotourism, provision of public services for communities in the park (especially waste management and health care) and establishing a more effective compensation or insurance system to offset economic losses due to wildlife damage.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".