From white to what? MENA and Iranian American non-white reflected race
Bibliographic record
Abstract
Whereas instruments like the US Census classify Middle Eastern and North African (MENA) Americans as white, racial formation-informed research has established that this population holds an ambiguous relationship with whiteness. I draw on theories of the self and cognition to introduce reflected race as an underexplored dimension of MENA racialization. Interviews with 84 Iranian Americans demonstrate how group members perceive they are appraised as distinct from and, in some ways, subordinate to a hegemonic US white norm. Following initial illegibility (“what?”) in racial appraisal, respondents perceive a classificatory splitting from whiteness and/or lumping with similarly racialized others. In other words, they micro-interactionally move from “white” to “what?” and ultimately, to an uncertain but deeply felt sense non-white reflected race. By turning attention to social-psychological-informed phenomenon like reflected race, researchers can make more full use of racialization and racial formation as the dynamic, multi-level concepts they were originally theorized to be.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".