Tourism Impacts and Support for Tourism Development in Ha Long Bay, Vietnam: An Examination of Residents’ Perceptions
Bibliographic record
Abstract
The impacts of tourism have been given much attention by scholars attempting to examine the perceptions as well as attitudes of the local residents toward tourism. Such studies have been carried out thoroughly in the context of the developed countries. However, very little research has been carried out in developing countries. This study attempts to make a little contribution to the sustainable development of tourism by examining the residents’ profile, perceptions and attitudes towards tourism impacts and tourism development in Ha Long Bay, the Vietnam’s first World Heritage Site (recognized in 1994). Data were collected by means of a questionnaire study. Based on 417 respondents surveyed, the findings show that the majority of respondents were young, Kinh rather than other ethnic group, they were married and were living in Ha Long Bay for over 20 years. On the whole, respondents viewed tourism positively and would support tourism development. They were generally in favor of tourism that contributes economically and socio-culturally to Ha Long Bay. They were, however, slightly ambivalent to environmental impacts of tourism. Implications, policy recommendations and limitations of study are presented in the conclusion.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".