The acculturation process: Antecedents, strategies, and outcomes
Bibliographic record
Abstract
With an increasingly integrated global economy, the need to understand how national work contexts impact newcomers is critical. In particular, it is important to understand individuals' possible responses to interaction within multicultural work contexts. Blending theoretical perspectives on social identity, cross‐cultural diversity, and identity formation/change, this paper explores the complex role played by dispositional and situational factors on acculturation strategies and, in turn, labour market outcomes. To guide this exploration, a theoretical model depicting the acculturation process is developed and presented. We posit that the relationship between cultural identity salience and acculturation strategy will be moderated by two key factors: desire for economic rewards and relational pressures. We further propose that acculturation strategy will influence the social networks and organizations that newcomers join, while these latter choices can help predict their income, employability, and advancement. We advance a number of testable propositions to stimulate future research and conclude with a discussion of the theoretical and practical contributions. Practitioner Points Recruitment processes should attract job applications from individuals with varying levels of cultural identity salience through wide‐reaching job marketing campaigns. Workgroup composition should reflect a diverse range of complementary skills. Performance management and reward systems should reward employees for idea‐sharing and achieving team goals. HRM strategy should seek to gain a competitive advantage through fostering diverse skills.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".