Bibliographic record
Abstract
Racial disparities in health are large and persistent. While decades of research have clearly established race-based disparities in health, less attention has been paid to the way in which race is conceptualized and measured in the study of health disparities. The three empirical studies comprising this dissertation move beyond current sociological work in this area by complicating how race is used in health disparities research. First, I consider the simultaneous effects of race and skin tone stratification in the patterning of physical health. This paper offers a new approach that better specifies the contribution of each status to health inequality, by providing a method that partitions the impact of race from skin tone in the same analysis. Second, I disaggregate combinations of mixed-race groups (i.e. Asian-White, Black-White, and Black-Asian) to better understand whether the social distribution of mental and physical health outcomes varies by specific combinations of mixed-race relative to monoracial groups. Finally, I consider whether Black-White differences in mental disorder have changed across cohorts of Black and White Americans – introducing social change into the study of racial disparities in health. Taken together, this dissertation demonstrates how thinking about race in more specific and complex ways allow us to uncover new empirical realities.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".