Towards Sickness: Developing a Critical Disability Archival Methodology
Bibliographic record
Abstract
Although archival records on disability—such as medical, institutional, and freak show records—can facilitate in telling one side of disability history, these records often omit the voices of disabled people. Considering the abundance of such documentation as well as how sick and disabled people may be difficult to locate in historical records, this article trains a critical lens on archival absences and partialities. By foregrounding the experiences of sick and disabled writers, activists, artists, and scholars alongside critical disability studies, this article conceptualizes “sickness” to develop a critical disability archival methodology. By illuminating the various ways in which sickness and disability can be unknowable and fluctuating, this article addresses the multiple, often illegible, layers of absences, subtleties, inaccuracies, and perspectives that are embodied in records, archives, and the lack thereof. A critical disability archival methodology underscores not only the multiple systems—social, institutional, colonial etc.—that have produced records about disabled people, but also the granular ways in which such values and absences are also created and embodied within archives and their processes. This methodology therefore provides a framework for both archivists and archival users to work in solidarity with sick and disabled communities in addressing archival representation.
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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 | 0.091 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.005 |
| Science and technology studies | 0.017 | 0.055 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".