The Substantially Impaired Sex: Uncovering the Gendered Nature of Disability Discrimination
Bibliographic record
Abstract
Intersectionality implies that employment discrimination is compounded or exacerbated in the presence of multiple protected statuses; in other words, the whole discrimination experienced by a multiple-protected-status individual is more than the sum of its parts.11 To the extent that intersectionality ex-7.Cf.Hill v. Lockheed Martin Logistics Mgmt., Inc., 354 F.3d 277, 283 (4th Cir.2004) (en banc) (demonstrating one federal court's struggle to differentiate between age discrimination and sex discrimination when the plaintiff 's primary evidence was that she had been called "useless old lady" by her supervisors).8. This example is one used by the United Nations in its disability equality training manual on defeating harmful stereotypes of disabled individuals.See LIZ CARR ET AL., DISABILITY EQUALITY TRAINING: ACTION FOR CHANGE 73 (2012), http://www.un.org/disabilities/documents/egms/2015/Kenji_Kuno_Change.pdf(citing the image of a "crippled witch" as one of the ways in which the "word disabled becomes synonymous for impotence, hopelessness and social inadequacy").9. This example is one used as an illustrative example of discrimination by the Labor Law Center on its Human Resources Blog.See Insults in the Workplace, LABOR LAW CTR.:
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".