Bibliographic record
Abstract
This dissertation looks at what factors affect the growth and lasting nature of MICE (Meetings, Incentives, Conferences, and Exhibitions) tourism. It particularly examines how different views and experiences of stakeholders impact the planning and management of MICE destinations. Using a mixed-methods strategy, the study combines qualitative information from detailed interviews with stakeholders and quantitative information from thorough industry surveys. Important findings show that different attitudes of stakeholders play a big role in shaping how MICE events are run and marketed, especially with a focus on public health and safety measures after the pandemic. These findings highlight the need to align what stakeholders want with their expectations to increase the success of MICE tourism, especially in healthcare, where conferences and events are key for sharing knowledge and networking in medicine. The results of this research go beyond MICE tourism, suggesting that understanding stakeholder dynamics can help inform broader strategies in the healthcare sector, supporting resilience and flexibility as public health issues become more important in event planning. By showing the links between stakeholder views and the sustainability of MICE tourism, this research aims to provide a better understanding of the challenges and opportunities in combining healthcare with event tourism, pushing towards a more responsible and sustainable MICE environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.545 | 0.402 |
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".