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Record W4362698565 · doi:10.5430/wjel.v13n5p310

The Elders’ Voices Regarding Their Role as a Catalytic Agent for Local Tourism

2023· article· en· W4362698565 on OpenAlexvenueno aff
Singkham Rakpa, Khomkrit Tachom, Budsaba Kanoksilapatharm, Albert Lisec

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsnot available
FundersUniversity of Phayao
KeywordsTourismSustainabilityLocal languagePsychologyTraining (meteorology)Identity (music)Local communityPublic relationsBusinessSociologyPolitical scienceGeography

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.276
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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