Creating, archiving and exhibiting disability history: The oral histories of disability activists of the Carleton University Disability Research Group
Bibliographic record
Abstract
Building a disability archives that is accessible is an ongoing challenge. At Carleton University in Ottawa, Canada, this work began a decade ago with the formation of a modest collection of scholars interested in disability issues. The Carleton University Disability Research Group developed as a collective of scholars, graduate students, and non-governmental organisation workers from the fields of social work, engineering, history, library, and archives, including people with disabilities. Since 2013, it has worked to collect, archive, discuss and display histories of disability in Canada, using various media. This paper documents and analyzes the aspects of this work linked to information studies, from the role of archivists and librarians to the making of archives and exhibits with, for, and about people with disability. It presents innovative decisions, introduces unexpected benefits for all in the light of the project of a critical disability archival method and discusses the potential of universities as a site of practice. It takes its most recent project, the Oral histories of activists in the disability rights movement in Canada (1970–2020) as the main case.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.051 | 0.036 |
| Scholarly communication | 0.025 | 0.007 |
| Open science | 0.002 | 0.013 |
| 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".