Congestion, popular world heritage tourist attractions and tourism stakeholder responses in Macao
Bibliographic record
Abstract
Purpose The purpose of this study is to offer advice to the Macao Special Administrative Region (SAR) Government regarding the tourist and traffic flow concerning how these contribute to congestion in World Heritage Site (WHS) elements and make recommendations regarding the management of tourist flows and traffic congestion. Design/methodology/approach The research undertaken in this study is partially longitudinal. The case study is also partially ethnographic in that tourist behaviour at key sites has been observed. Concerning the specific methodology applied, data collection techniques are chosen to provide a multiplicity of data sources: on-site observation and semi-structured telephone interviews. Findings The study is found that Macao was at a crossroad. All stakeholders needed to take some responsibility for implementing actions recommended that would ensure that Macao SAR’s World Heritage assets would be used responsibly for future, as well as for present generations. Originality/value The study has shown that better and long-term understanding of congestion is necessary to inform better visitor management decision-making, enhance tourist experience and discover the factors that influence visitor satisfaction. It is also needed to reveal aspects of stakeholder readiness and barriers to action.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".