Revitalising small tourism destination states: necessity and strategies for structural change in tourism development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper delves into the controversy surrounding the link between structural change and tourism development, particularly focusing on small tourism states, over the period 1995Q1–2020Q4 using a panel-based approach. To address the research objective, the Quantile-on-Quantile (QQ) regression approach is utilised to assess the impact of different quantiles of structural change on the quantiles of tourism development. To ensure robustness, the outcomes of the QQR approach are compared with those of the conventional quantile regression technique. Empirical findings from the QQR approach reveal nuanced relationships. In Belize, Cyprus, Dominican Republic, Iceland, Malta, and Seychelles, structural changes exhibit a structural-increasing effect on tourism development. Conversely, in the Bahamas, Fiji, and Trinidad & Tobago, structural changes manifest both structural-increasing and structural-decreasing effects on tourism development. In Bahrain, structural changes have a structural-decreasing impact on tourism development. Furthermore, the study establishes a feedback nexus between tourism and structural change, offering feasible policy suggestions for policymakers. Overall, the findings suggest that sound policy responses to the relationship between structural change and tourism development should be context-specific, responsive to changing dynamics, and aimed at maximising the positive impacts of structural changes while mitigating potential negative consequences.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it