Middle Eastern and North African Americans may not be perceived, nor perceive themselves, to be White
Bibliographic record
Abstract
People of Middle Eastern and North African (MENA) descent are categorized as non-White in many Western countries but counted as White on the US Census. Yet, it is not clear that MENA people see themselves or are seen by others as White. We examine both sides of this ethnoracial boundary in two experiments. First, we examined how non-MENA White and MENA individuals perceive the racial status of MENA traits (external categorization), and then, how MENA individuals identify themselves (self-identification). We found non-MENA Whites and MENAs consider MENA-related traits-including ancestry, names, and religion-to be MENA rather than White. Furthermore, when given the option, most MENA individuals self-identify as MENA or as MENA and White, particularly second-generation individuals and those who identify as Muslim. In addition, MENAs who perceive more anti-MENA discrimination are more likely to embrace a MENA identity, which suggests that perceived racial hostility may be activating a stronger group identity. Our findings provide evidence about the suitability of adding a separate MENA label to the race/ethnicity identification question in the US Census, and suggest MENAs' official designation as White may not correspond to their lived experiences nor to others' perceptions. As long as MENA Americans remain aggregated with Whites, potential inequalities they face will remain hidden.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".