Multicultural identity integration and well-being: a qualitative exploration of variations in narrative coherence and multicultural identification
Bibliographic record
Abstract
Understanding the experiences of multicultural individuals is vital in our diverse populations. Multicultural people often need to navigate the different norms and values associated with their multiple cultural identities. Recent research on multicultural identification has focused on how individuals with multiple cultural groups manage these different identities within the self, and how this process predicts well-being. The current study built on this research by using a qualitative method to examine the process of configuring one's identities within the self. The present study employed three of the four different multiple identity configurations in Amiot et al. (2007) cognitive-developmental model of social identity integration: categorization, where people identify with one of their cultural groups over others; compartmentalization, where individuals maintain multiple, separate identities within themselves; and integration, where people link their multiple cultural identities. Life narratives were used to investigate the relationship between each of these configurations and well-being, as indicated by narrative coherence. It was expected that individuals with integrated cultural identities would report greater narrative coherence than individuals who compartmentalized and categorized their cultural identities. For all twenty-two participants, identity integration was significantly and positively related to narrative coherence, while compartmentalization was significantly and negatively related to narrative coherence. ANOVAs revealed that integrated and categorized participants reported significantly greater narrative coherence than compartmentalized participants. These findings are discussed in light of previous research on multicultural identity integration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 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".