Tourism Well-Being and Transitioning Island Destinations for Sustainable Development
Bibliographic record
Abstract
The unprecedented growth of tourism over the last century has led to increasing concerns over the sustainable development of many popular tourism destinations throughout the globe. High concentrations of tourists and residents, especially in urbanized areas, have heightened this concern with the arrival of the novel coronavirus (COVID-19) pandemic. Over reliance on tourism has left residents vulnerable to external factors, such as the coronavirus pandemic that has halted tourists from coming to this remote destination. As a result, Hawaii’s overall economy is suffering greatly. A survey was developed and distributed to potential tourists in order to acquire their perceptions regarding tourism and well-being, as well as the COVID-19 outbreak. The focus of this study was to examine practices in tourism that moves beyond solely economics which will allow repositioning in a manner that promotes the well-being of both residents and tourists and to transition this unique tourism destination for sustainable development practices for the future. One of the results from the study reported that the majority of the respondents agreed or strongly agreed that testing for COVID-19 should be a travel requirement prior to flying to Hawaii, as well as having an additional COVID-19 test administered upon arrival.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".