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Record W2163038337 · doi:10.1177/1059601105275266

Domain and Development of Cultural Intelligence

2005· article· en· W2163038337 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGroup & Organization Management · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsConceptualizationCultural intelligenceFacet (psychology)Construct (python library)PsychologyDomain (mathematical analysis)Cognitive scienceCognitionComponent (thermodynamics)Cognitive psychologyKey (lock)Domain specificityEmotional intelligenceKnowledge managementComputer scienceEpistemologySocial psychologyArtificial intelligencePersonalityBig Five personality traits

Abstract

fetched live from OpenAlex

The potential for defining a reliable measure of a cross-cultural facet of intelligence has enormous implications for explaining and predicting the increasingly prevalent cross-cultural interactions that occur in business settings. In this article, the author presents a definition of cultural intelligence (CQ) that explicitly introduces the concept of mindfulness as a key component that links knowledge with behavioral capability. It builds on previous definitions by grounding the conceptualization in the cognitive domain and differentiating CQ as a capability that includes skilled behavior. However, alternatives to previous conceptualizations with regard to the constituent elements and their relationship to each other are presented with a view toward a tighter specification of the construct. Also, a developmental stage model of CQ is outlined. Implications for the assessment of CQ are discussed.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.344

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)0.0000.000

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 head0.023
GPT teacher head0.297
Teacher spread0.274 · 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