Bibliographic record
Abstract
Governance is recognized as a means to promote sustainable outcomes by democratizing the policy process and potentially harmonizing competing policy interests. This is particularly critical for sustainable tourism policy with its multiple sectors and multiple stakeholders at multiple scales. Yet little is known about the kinds of governance processes and instruments that are able to effectively harmonize competing power interests to better balance economic, ecological, and social concerns. This study analyzes the case of Bhutan and its Gross National Happiness (GNH) strategy as it is applied to sustainable tourism policy. Based on semi-structured interviews and focus groups with 57 state and non-state governance actors, it explores whether Bhutan’s unique GNH governance framework successfully harmonizes competing interests in the pursuit of sustainable tourism policy. It argues that the implementation of Bhutanese tourism policy is characterized by diverse and unexpected applications of power by multiple policy stakeholders. These complex power dynamics are not shaped in a meaningful way by the GNH governance instruments. Nor are they rooted in a common understanding of GNH itself. While this situation should subvert sustainable tourism policy, a commitment among state and non-state governance actors to a common set of Buddhist-infused cultural values shapes and constrains policy actions in a manner that promotes sustainable tourism outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".