Navigating Differential Micro-Racialization in the United States and Canada: A Mixed-Method Exploration of Multiethnic-Racial Individuals’ Malleable Racial Identification Strategies
Bibliographic record
Abstract
Because race is a primary identity category in North America, individuals are racialized by those they interact with on a daily basis. However, multiethnic-racial individuals, those with parents from different ethnic-racial backgrounds, often face differential micro-racialization across daily encounters, meaning that at some times they are categorized as belonging to one ethnic-racial group, and at other times they are categorized into a different ethnic-racial group. To meet this fluid categorization, multiethnic-racial individuals often employ malleable racial identification strategies wherein they shift their cognitive, communicative, and labeling behaviors to meet the demands of the changing racialized context. This study employs a concurrent mixed method design to explore how multiethnic-racial individuals navigate differential micro-racialization across their interpersonal interactions, and, how their navigation of these racialized contexts implicates their psychological wellbeing. Taken together, the quantitative and qualitative results suggest an inverse relationship between malleable racial identification and psychological wellbeing that could be due to participants’ (1) cognitive load associated with revealing or concealing their identities, or (2) negative emotions that stem from their interpretations of these identity shifts. Implications and opportunities for future research are discussed.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".