Stakeholder collaboration: A means to the success of rural tourism destinations? A critical evaluation of the existence of stakeholder collaboration within the Mournes, Northern Ireland
Bibliographic record
Abstract
Tourism has long been pursued by governments as a means of development in rural areas. Negatively, rural areas have certain characteristics that inhibit their ability to achieve the full benefits of tourism. Consequently, many rural tourism destinations to date have found that the benefits to be gained are over-stated. Stakeholder collaboration has been deemed critical for the success of sustainable tourism. In fact, in the context of rural tourism destinations stakeholder collaboration can be particularly advantageous in addressing specific factors relevant to rural tourism destinations that may inhibit the success of the destination. However, successful attempts to implement stakeholder collaboration have been limited. A growing body of literature reveals that successful stakeholder collaboration relies on numerous elements, which have to be incorporated for the success of the process. The paper reveals how simply attempting to implement stakeholder collaboration is not enough for its success, instead various components need to be incorporated throughout the continuous process, in particular attention is paid to establishing trust across the various stakeholder group.
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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 | 0.058 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.019 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| 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".