Willingness to Learn: Cultural Intelligence Effect on Perspective Taking and Multicultural Creativity
Bibliographic record
Abstract
Technological development has intensified interconnectivity in the global sphere creating highly diverse markets and workplaces making increasingly challenging for contemporary organizations to manage culturally diverse environments while benefiting from them. Hence, fostering employees’ ability to produce both novel and useful ideas within cross-cultural environments has gained enormous importance. This research attempts to better understand the relationship between cultural intelligence (CQ), perspective taking, and multicultural creativity. Data analysis from a causal, descriptive, non-experimental network survey, containing a remote associates test, supports the proposed theoretical framework in which cultural intelligence has an influence on the relationship between perspective taking and the individuals’ capability of drawing upon knowledge from distinct cultures. The results of the study show that two dimensions of cultural intelligence, motivational CQ and behavioral CQ, positively influence individuals’ multicultural creativity. These findings have positive implications when facing the urgent necessity of cross-cultural collaboration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".