Integrating Dynamic Governance into Sustainable Tourism Management: A Framework For Socio-Ecological Development
Bibliographic record
Abstract
This study aims at the direct and indirect consequences of adaptability, integration, and visioning, which are fundamental components of dynamic governance, on socio-ecological development and sustainable tourism.Data were obtained from 200 respondents with varied demographic backgrounds using a quantitative technique and Partial Least Squares Structural Equation Modeling (PLS-SEM).The results show ten statistically significant relationships: seven have positive impacts, while three have paradoxical negative connections.Notably, adaptiveness improves sustainable tourism while harming socio-ecological development, implying a trade-off between flexibility and ecological balance.Furthermore, socio-ecological development has a detrimental influence on tourist sustainability, highlighting possible contradictions between conservation aims and tourism expansion.The study contributes to the theoretical integration of governance dynamics and sustainability while also providing practical insights for aligning development agendas.It emphasizes the importance of collaborative, adaptable, and forward-thinking governance approaches that bridge environmental and economic goals in tourist planning.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".