Racial Mismatch: The Divergence Between Form and Function in Data for Monitoring Racial Discrimination of Hispanics<sup>*</sup>
Bibliographic record
Abstract
Objectives. A primary justification for collecting U.S. racial statistics is the need to monitor racial discrimination. This article aims to show how analyses of Hispanics—who may officially be of any race—tend to miss discrimination based on racial appearance by relying on data that instead capture racial self‐identification, a different aspect of race that often does not correspond. Methods. The study analyzes 60 qualitative interviews with Dominican and Puerto Rican migrants in the New York metropolitan area. It employs multiple measures to represent theoretically distinct aspects of the lived experience of race. Results. Respondents interpret the Census race question in different ways corresponding to different aspects of race, which often do not match one another. Although respondents experience discrimination on the basis of phenotype, their racial self‐identification is a poor proxy for measuring their racial appearance. Conclusions. Scholars need to develop a language of race that communicates the multiplicity of social processes involved. Social surveys must provide measures of these multiple components, including interviewer observations of racial appearance, to monitor discrimination on the basis of phenotype within Hispanic groups.
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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 | 0.084 | 0.357 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".