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Record W2003279550 · doi:10.1177/1470595807079385

Which Is Easier, Adjusting to a Similar or to a Dissimilar Culture?

2007· article· en· W2003279550 on OpenAlexaboutno aff
Jan Selmer

Bibliographic record

VenueInternational Journal of Cross Cultural Management · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsSocial psychologyPsychologyPropositionExploratory researchSimilarity (geometry)Survey data collectionCultural diversitySociologySocial scienceMathematicsComputer scienceEpistemology

Abstract

fetched live from OpenAlex

The intuitively paradoxical research proposition that it could be as difficult for business expatriates to adjust to a similar as to a dissimilar host culture is tested in this exploratory study. Based on data from a mail survey, a comparison of American business expatriates in Canada and Germany respectively did not reveal any difference in their extent of adjustment. Besides a significant between-group difference in cultural distance, confirming that the American expatriates perceived Canada as more culturally similar to America than Germany, no significant intergroup differences were detected for general adjustment, interaction adjustment, work adjustment and psychological adjustment. Neither was there a difference in the time-related variable; time to proficiency. Although highly tentative, the suggestion that the degree of cultural similarity/dissimilarity may be irrelevant as to how easily expatriates adjust is fundamental. Implications for theory, practice and future research of these findings are discussed in detail.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.054
GPT teacher head0.444
Teacher spread0.390 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations89
Published2007
Admission routes1
Has abstractyes

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