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Record W2893792371 · doi:10.1108/jieb-12-2016-0048

Cultural intelligence and mindfulness: teaching MBAs in Iran

2018· article· en· W2893792371 on OpenAlexaffabout
David Cray, Ruth McKay, Robert Mittelman

Bibliographic record

VenueJournal of International Education in Business · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsCarleton UniversityRoyal Roads University
Fundersnot available
KeywordsExpatriateCultural intelligenceOriginalityContext (archaeology)MindfulnessValue (mathematics)Flexibility (engineering)PsychologyPedagogySociologyPolitical scienceSocial psychologyManagementCreativityGeography

Abstract

fetched live from OpenAlex

Purpose A dynamic global economy has increased the need for cross-cultural flexibility and cultural intelligence (CQ). While a large literature has examined various means to increase CQ in student and expatriate populations, its importance for teachers in cross-cultural settings has been largely unexamined. This paper aims to use the experiences of a group of professors in an MBA programme in Iran to investigate the effect of their activity on their cross-cultural skills. Design/methodology/approach Using structured interviews and content analysis, the authors draw on the experiences of business faculty from a Canadian business school who helped deliver an MBA programme in Iran to investigate how their experiences in a country new to them were reflected in the components of CQ. Findings Using an established model of CQ, the authors find contributions to all three facets, knowledge, mindfulness and behaviour, indicating that such exchanges can be regarded as important for students and teachers alike in an international educational context. Originality/value With more and more teaching extending across cultural boundaries in both domestic and international settings, the capacity of instructors to read, interpret and react to the attitudes, beliefs and behaviours of their students is an important factor in the success of these programs. To this point, at least within the business education literature, the influence of such encounters on the instructors involved has been neglected.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.054
GPT teacher head0.411
Teacher spread0.357 · 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 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

Citations8
Published2018
Admission routes2
Has abstractyes

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