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Record W4402046999 · doi:10.5539/ies.v17n5p1

Student’s Lifestyles: Cross Cultural Research (Thailand and Australia)

2024· article· en· W4402046999 on OpenAlexvenueno aff
Rungson Chomeya, Araya Piyakun, Gunniga Phansri

Bibliographic record

VenueInternational Education Studies · 2024
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
FundersMahasarakham University
KeywordsCross-culturalSociologyCultural influenceCultural diversityPsychologyPedagogyMathematics educationSocial scienceAnthropology

Abstract

fetched live from OpenAlex

This study aimed to examine and compare the lifestyles of Australian and Thai undergraduates. There were 213 students who participated in the study; 112 were Thai and 101 were Australian. The instrument included a questionnaire containing six lifestyle categories. Cronbach’s alpha coefficient revealed that the questionnaire’s reliability ranged from 0.71 to 0.92. The data were analyzed using mean, standard deviation, and t-test. The result indicated that the overall lifestyle score of students from both countries was moderate. However, there were differences between the two groups’ lifestyles. The Australian participants favored the healthy lifestyle, whereas the Thai participants favored the conservative and homey lifestyles. Interestingly, the Australian participants’ trendy lifestyle score was the lowest, while the Thai participants’ night going lifestyle score was the highest. Comparing the two groups revealed a statistically significant difference of 0.05. This indicated that the Thai participants’ lifestyles were more apparent than those of their counterparts. Only the night going lifestyle and the healthy lifestyle were rated higher by Australians than by Thais. The findings illustrate the differences in lifestyles between two countries, reflecting the complexity of lifestyle development in various dimensions. Understanding this complexity is therefore crucial.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.446
GPT teacher head0.653
Teacher spread0.207 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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