Needing to Acquire a Physical Impairment/Disability: (Re)Thinking the Connections between Trans and Disability Studies through Transability
Bibliographic record
Abstract
This article discusses the acquisition of a physical impairment/disability through voluntary body modification, or transability. From the perspectives of critical genealogy and feminist intersectional analysis, the article considers the ability and cis*/trans* axes in order to question the boundaries between trans and transabled experience and examines two assumptions impeding the conceptualization of their placement on the same continuum: 1) trans studies assumes an able‐bodied trans identity and able‐bodied trans subject of analysis; and 2) disability studies assumes a cis* disabled identity. The perception of transsexuality and transability as mutually exclusive phenomena results from a nonintersectional analysis of transsexuality as an issue of sex/gender, but not of ability, and of transability as an issue of ability, but not of sex/gender. Difficulty recognizing continuities between these phenomena thus stems from an ableist interpretation of sex/gender and a cis(gender)normative* interpretation of ability. This article aims to: 1) enrich intersectional analysis in trans and disability studies and transability scholarship; 2) complicate disability studies, in which disabilities are often presumed to be “involuntary,” and encourage the decentering of a cis* subject; 3) encourage trans studies to decenter an able‐bodied subject; and 4) advocate for increased dialogue and the creation of alliances between trans and disability studies and movements.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.072 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| 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".