Hierarchies of Categorical Disadvantage: Economic Insecurity at the Intersection of Disability, Gender, and Race
Bibliographic record
Abstract
Intersectional feminist scholars emphasize how overlapping systems of oppression structure gender inequality, but in focusing on the gendered, classed, and racialized bases of stratification, many often overlook disability as an important social category in determining economic outcomes. This is a significant omission given that disability severely limits opportunities and contributes to cumulative disadvantage. We draw from feminist disability and intersectional theories to account for how disability intersects with gender, race, and education to produce economic insecurity. The findings from our analyses of 2015 American Community Survey data provide strong empirical support for hierarchies of disadvantage, where women and racial minority groups with disabilities and less education experience the highest poverty levels, report the lowest total income, and have a greater reliance on sources outside the labor market for economic security. By taking disability into account, our study demonstrates how these multiple characteristics lead to overlapping oppressions that become embedded and reproduced within the larger social structure.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".