Unboxing Disability Communication: Exploring the Methodological Contours of Critical Disability Research in Public Health Archives
Bibliographic record
Abstract
Concerned by the absence of communication with and to people with disabilities during the COVID-19 pandemic, we turned to the School of Public Health material archived at the University of Toronto to uncover preserved stories of previous public health crises. This article maps some of the methodological contours of researching disability communication as we excavated material from brown boxes wheeled out on trolleys by university archivists. Our methodology resists bounded chronology to trace new communicative connections across an array of material: letters, photographs, research grants, and public health lectures. We contrast the plentiful abundance of ableist internal departmental communication that is preserved in the archives with the illegible paucity of external communication to and about people with disabilities. Embracing the minutiae of ways such communication of disability is present and absent within the archives – a Newfie joke, an academic world tour, or a health promotion pamphlet addressed to an “ordinary housewife”, we reconsider the meaning of disability. We attend to our own embodied acts of communication: reading, photographing, pasting, labelling, and assembling seemingly tangential documents from across hundreds of boxes while simultaneously acknowledging the surveillance work of the archive and our own preservation of some materials and procedures. Revealing the possibilities of an interpretive disability studies approach, we map methodological contours we used to open three ways to address dominant narratives about public health communication relative to disabled people: re-orienting, re-encountering, and re-searching.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.348 | 0.366 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.023 | 0.012 |
| Science and technology studies | 0.055 | 0.219 |
| Scholarly communication | 0.056 | 0.049 |
| Open science | 0.011 | 0.051 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".