Tourism destination image development: a lesson from Macau
Bibliographic record
Abstract
Purpose – Drawing upon an analysis of resident and visitor survey data and Macau Government Tourist Office (MGTO) press releases in 2012, the purpose of this paper is to understand the tourism destination image for this tourist historic city produced by these three key stakeholder groups in Macau. Design/methodology/approach – This is achieved using a new stakeholder analysis tool, developed from previous studies, which compares the perspective of the MGTO, the city’s destination marketing organization, with that of its residents and visitors. This study examines the perceptions that residents and visitors have about the general images projected and generated in Macau. Findings – This research highlights the multiplicity of images and producers of images in Macau. Originality/value – The lesson from this case study is that public sector agencies need to acknowledge more clearly the tourism planning role of the host community in particular. The possibility of detecting disconnections and misalignments of shared destination imagery by residents and visitors has implications for the public sector in Macau and other destinations in relation to managing and developing a destination and contributes to a greater understanding of stakeholders and sustainable tourism development overall.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".