The neglected role of cultural intelligence in recent immigrant newcomers’ socialization
Bibliographic record
Abstract
The purpose of this article is to investigate the role of cultural intelligence (CQ) in contributing to the socialization of recent immigrant newcomers (RINs). Drawing on relevant literatures, a conceptual model is developed, highlighting the role of RINs’ CQ in helping them choose the appropriate adjustment strategies that in turn allow them to better perform their job and to socially integrate into their workplace. The article also examines the impact of the social context of the organization, namely the level of diversity, specifically focusing on how RINs may choose different adjustment strategies depending on the type of organizational context and according to the variance in their CQ. Thus, the article makes three important contributions. First, the article integrates CQ literature with immigrant and socialization literatures by exploring the process through which RINs’ CQ can enhance their role performance and social integration during socialization. Second, at the individual level, RINs may find the analysis useful in comprehending the role of CQ for understanding cultural nuances and developing relationships with their new work colleagues, and this may motivate them to further develop their CQ. Third, organizations may consider providing RINs—as well as other employees—with cross-cultural training incorporating CQ modules to enhance and improve their CQ and thereby optimize RINs’ organizational socialization.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".