Bibliographic record
Abstract
Cripping Girlhood offers a new theorization of disabled girlhood, tracing how and why representations of disabled girls emerge with frequency in twenty-first century U.S. media culture. It uncovers how the exceptional figure of the disabled girl most often appears as a resource to work through post-Americans with Disabilities Act (ADA) anxieties about the family, healthcare, labor, citizenship, and the precarity of the bodymind. In paying critical attention to disabled girlhood, the book uses feminist disability studies to rupture the unwitting assumption in girls’ studies that girlhood is necessarily non-disabled. By closely examining the ways that disabled girls represent themselves, Anastasia Todd goes beyond a critique of the figure of the privileged, disabled girl subject in the national imagination to explore how disabled girls circulate their own capacious re-envisioning of what it means to be a disabled girl. In analyzing a range of cultural sites, including YouTube, TikTok, documentaries, and GoFundMe campaigns, Todd shows how disabled girls actively upend what we think we know about them and their experience, recasting the meanings ascribed to their bodyminds in their own terms. By analyzing disabled girls’ self-representational practices and cultural productions, Todd shows how disabled girls deftly theorize their experiences of ableism, sexism, racism, and ageism, and cultivate communities online, creating archives of disability knowledge and politicizing other disabled people in the process.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".