Operationalizing the social-ecological systems framework in a protected area: a case study of Qilian Mountain National Park, Northwestern China
Bibliographic record
Abstract
The theory of social-ecological systems (SESs) provides an ideal tool for understanding complex human-nature systems in protected areas. We adopt Ostrom’s SESs framework to analyze the complex interactions within Qilian Mountain National Park (QMNP), an essential ecological security barrier in China. Through qualitative and quantitative analyses of action situations, we identify the interactions among the resource system, resource units, governance system, and actors. The results show that applying the SESF in protected areas is feasible and operable; the development level of ecosystems in QMNP is better than that of the social systems; and the coupling coordination among the four subsystems is at primary coordination (0.6–0.7), indicating that the interaction between four subsystems needs to be strengthened. Moreover, the comprehensive evaluation index and the interactions between subsystems differ among townships, indicating the necessity for tailored management strategies. We emphasize integrating social components into protected areas management to achieve sustainable development and resilience in SESs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".