Bibliographic record
Abstract
This paper presents lessons learned from a project titled Advocates Assembly: Disability Research from the Ground Up. Hosted by Ontario Tech University’s Institute for Disability and Rehabilitation Research (IDRR), the series featured representatives from disability-led activist and rights advocacy groups and disability-focused healthcare and social services providers. Across the series, speakers collectively articulated a number of key principles that ground disability research in community. In this paper the authors organize and analyze themes from these events, to show how community-based research principles have the power to confront and transform institutionally entrenched forms of disability research. Those principles are as follows: researchers should seek out contextually specific knowledge, researchers should unsettle their role as experts, researchers should acknowledge and enact their accountability, and researchers should commit to sustainable and transformational change, which challenges the temporal parameters to projects.
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 | 0.160 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.022 | 0.066 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".