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Record W1882671101

Cross Cultural Dimensions of Cultural Capital: A Comparison Between Greece and Turkey

2015· article· en· W1882671101 on OpenAlexvenueno aff
Mehmet Ali Özçobanlar, omasz Ochinowski, Bülent Açma

Bibliographic record

VenueCanadian social science · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsTurkishHappinessPoliticsSocial capitalCultural capitalLife satisfactionPsychologySociologyPolitical scienceSocial psychologySocial scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

In this research, cross cultural dimensions between two ancient societies, Greece and Turkey have been examined and compared. In addition, West and Anatolia were also compared based on selected cross cultural dimensions. Cross cultural dimensions were evaluated in 11 factors: Education, Political Trust, Voluntary Work, Political Participation, Life Satisfaction Component, and Trust in People, Fear of Others, Life Satisfaction, Happiness, Income and Deprivation Index. In the research, a rhetorical analysis was also performed based on interviews with selected organizations in Greece and Turkey. According to results of the research, all 11 factors of cross cultural dimension were statistically significant between two countries (p<.05). Education levels were higher in Greece. Turkish people trust more political institutions. Greek people were less volunteering for community and social services. Political participation was higher in Turkish participants. Greek participants were more satisfied with education, accommodation, health and social life. Greek participants had less trust to other people. Life satisfaction level was higher in Turkish participants. Happiness levels of Greek participants were also lower. Income levels of Greek participants were lower, where deprivation index was higher in Turkish participants.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.005
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.065
GPT teacher head0.367
Teacher spread0.302 · 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 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
Published2015
Admission routes1
Has abstractyes

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