The Elders’ Voices Regarding Their Role as a Catalytic Agent for Local Tourism
Bibliographic record
Abstract
Elderly populations play important roles in society as providers of historical accounts, wisdom, culture, traditions, and social customs. As elderly people generally remain physically, mentally, and intellectually healthy, encouraging them to preserve and share their local identity through English language communication is one strategy that can support Thailand’s Sustainable Development Goals (SDGs) for older people. This study investigates the elderly’s need to learn communicative English for tourism to maintain local cultural identities. The participants included 170 retired elders from one district in a province in northern Thailand. The instruments used for data collection were questionnaires and semi-structured interviews. The results indicated that 65.88 % of elders expressed a need for English language training for tourism communication. The elders’ overall needs for training content were also at a high level (x̅= 3.69). Finally, the elders’ needs for situations for practicing English communication skills were at a high level (x̅= 3.74). The results of this study suggest that it is vital to survey and prioritize the elders’ needs for English language communication training courses for local community sustainability. The results of the present study have been used as guidelines for training elders to strengthen their role as a catalytic agent for local tourism.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".