Using Duoethnography to Connect the Disability Justice Principles to Education Research about Disabled Populations on Campus
Bibliographic record
Abstract
The terms disability inclusion, disability rights, and disability justice are often used somewhat interchangeably, but have distinct meanings within academe more broadly and academic research contexts. The purpose of this investigation was to explore these concepts in relation to our research and present the way in which we (as education researchers) grappled with what a critical, disability justice-informed research methodology involves. We used a qualitative, duoethnographic research approach as it is both a reflection of social justice and a method to advance it (Sawyer & Norris, 2013). We engaged in virtual, asynchronous and synchronous dialogues in writing and audio formats to reflect, critique, question, and eventually, generate new ideas and ways of moving forward. In the paper, we first consider how the Disability Justice Principles from Sins Invalid (2019) could be connected to our current research practices using two questions about ethical considerations as well as research methodologies and frameworks. We then theorize how education researchers can intentionally incorporate activism throughout each stage of the research process. A Disability Justice-informed education research framework is proposed for use with research about disabled populations in higher education. This framework addresses the relationship between stages of the research process, disability inclusion, and disability justice, which was the ongoing debate throughout our dialogues.
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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 | 0.033 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.021 | 0.060 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.005 | 0.008 |
| 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".