Managing protected areas for sustainable tourism: prospects for adaptive co-management
Bibliographic record
Abstract
This paper looks at the challenging enterprise of managing protected areas for sustainable tourism. It notes that during the past 25 years multistakeholder conflicts, complexity and uncertainty have emerged and persisted as important issues requiring managerial responses. These issues reflect substantial paradigmatic shifts in pursuing and understanding sustainability. Governance directs attention to broad participatory approaches, and complex systems theory emphasises transformative changes and an integrative perspective that couples human and natural systems (a social–ecological system). The paper envisions the prospects of adaptive co-management as an alternative approach to protected areas management for sustainable tourism. It also makes the case for an interdisciplinary approach by highlighting important and informative developments outside tourism studies. Adaptive co-management bridges governance and complex systems by bringing together cooperative and adaptive approaches to management. In appraising the potential for adaptive co-management attention is systematically directed to conceptual, technical, ethical and practical dimensions. While adaptive co-management is clearly not a universal answer, experiences and knowledge from natural resource management raise salient prospects for the approach to be insightfully applied to protected areas for sustainable tourism.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".