Differing Quality of Life by Understanding Alternative Personal Profiles of People in Community-Based Tourism, Thailand
Bibliographic record
Abstract
This research aimed to investigate the differences between individual factors affecting quality of life (QOL) for people conducting community-based tourism (CBT). A sample size of 200 comprised people in CBT, Thailand. The data were collected to achieve the research objective by studying the personal profiles of people in CBT including sex, age, education, occupation and income affecting quality of life. Other factors included physical conditions of individuals, psychological state, perception of the relationship between individuals and others and environment. The research employed descriptive and inferential statistics, the F test (one-way ANOVA), to evaluate the data. The results revealed that only education factor significantly differed at level 0.05. Conversely, the factors sex, age, occupation and income showed no significant differences at level 0.05. The result of a study indicates educational level was essential for QOL. Therefore, education, as the most significant factor, should be set as a priority to lead the planning process in various aspects of QOL. Even the community and society need to focus on educational factors leading to a higher QOL. The contribution of this research was to enhance education in society, particularly in CBT to all individuals in the community to obtain greater opportunity to equally access education.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".