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Record W2945689600

Cultural Intelligence Stimulating Professional Adjustment

2019· article· en· W2945689600 on OpenAlexaboutno aff
Neha Kaleramna, Teena Saharan, Upendra Singh

Bibliographic record

VenueJournal of economics and economic education research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsCultural intelligencePsychologyCognitionConsciousnessSocial psychologyPopulationApplied psychologyPublic relationsSociologyPolitical scienceDemography
DOInot available

Abstract

fetched live from OpenAlex

Contrary to earlier studies, this paper analyzed the influence of cultural intelligence (CQ) on professional adjustment of expatriates. The stimulators were four dimensions of CQ i.e. Cognitive, Meta-cognitive, motivational and Behavioral. The paper is quantitative in nature and a structured questionnaire was used to administer the survey. The target population of this research was senior and middle level Indian expatriates working with different companies in USA and Canada. The present research contradicted the earlier researches, which stated that cultural intelligence is helpful only in personal/social adjustment of expatriates not in professional adjustment. The results clearly indicated a positive relationship of motivational CQ and meta-cognitive CQ with professional adjustment of Indian expatriates. The results suggest that organizations must select those employees for expat assignments that long-for changes/challenges, and should train them to improve cross-cultural consciousness eventually leading to better professional adjustment at foreign locations.

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)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.128
GPT teacher head0.474
Teacher spread0.347 · 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 designNot applicable
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

Citations6
Published2019
Admission routes1
Has abstractyes

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