<i>n</i> -Culturals, the next cross-cultural challenge
Bibliographic record
Abstract
This article advances current conceptualizations of multicultural identities by identifying constituent elements of multicultural identity as knowledge, identification, internalization, and commitment. This new conceptualization is labeled n- Culturalism and posits that there are individuals who operate at the intersection of multiple cultures by maintaining salience of their multiple cultural identities. We illustrate that n-Culturals are assets to organizations because they are creative synthesizers that are able to facilitate organizational goals and can also serve as models for others who are struggling in a multicultural environment. This article provides some solutions to managing multicultural challenges in organizations, such as conflicting values and identities. It also offers solutions on how individuals and organizations can leverage their identities in relation to the multiculturalism continuum to achieve desired workplace outcomes. Further, we introduce the multicultural mentor modeling program for organizations, which, if implemented, can help struggling multiculturals to address challenges in their social cognition and to develop appropriate and effective behaviors in and outside of the workplace.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".