Cultural Intelligence: New Directions for Research in Asia
Bibliographic record
Abstract
Cultural intelligence is a relatively new area in cross-cultural research. In this paper we describe the construct of cultural intelligence (Cultural Quotient, CQ) and examine the need for more research on cultural intelligence in Asia. We propose that there are many areas that need to be examined as there is a dearth of research in the Asian countries on this subject. We identify specific cultural contexts and environments in Asia where future research on CQ could be done and justify why these specific cultural contexts in Asia may be relevant to CQ research. Four important areas that need to be examined, the measurement of CQ, CQ and its relation to culture-specific variables, CQ and Emotional labor and CQ Education and Training in Asia are briefly discussed. Some recommendations and new directions for future research which will enhance the knowledge basefor both CQ theory and CQ practice in Asia are also discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.013 | 0.029 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".