The impact of implicit social conflict on ecosystem conservation in protected areas: a case study of Patatso National Park
Bibliographic record
Abstract
Ecosystem conservation in protected areas (PAs) depends on friendly behavior (e.g., no illegal wildlife hunting) toward the ecosystem by residents living within a PA, which can be prompted by good cooperation with PA managers. However, conflicts often arise between local residents and PA managers (e.g., disproportionate allocation of benefits), existing in the form of implicit conflicts (e.g., non-compliance with policy implementation) that can affect conservation goals of a PA. Here, we develop a theoretical framework, synthesizing theories related to conflict, displaced aggression, routine activity, withdrawal behavior, and livelihood, to explain how implicit conflicts affect ecological misbehavior of residents living within PAs. We collected data from 155 residents living in Patatso National Park, China, during April–June 2022 to test the relationships between our framework variables (i.e., implicit conflicts, ecological misbehavior, withdrawal intention, livelihood strategies) using partial least squares-structural equation modeling. Results show that: (1) higher levels of implicit conflicts strengthen residents’ withdrawal intention from environmental responsibility and increase ecological misbehavior; (2) the effect of implicit conflicts on withdrawal intention and ecological misbehavior is moderated by the livelihood strategies of residents; (3) for residents adopting inner livelihood strategies (e.g., herding), higher implicit conflicts lead to stronger withdrawal intention and ecological misbehavior; for those adopting outer livelihood strategies (e.g., working in cities or industries), implicit conflicts have no significant effect on withdrawal intention or ecological misbehavior. This research elucidates the mechanisms by which social conflict influences residents’ ecological misbehavior by clarifying the mediating role of withdrawal intention and the moderating effects of livelihood strategies, offering practical insights for managers to enhance the effectiveness of ecosystem conservation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| 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".