Diverse Disability Identities: Gender and Sexuality, Racial and Ethnic Diversity, Indigeneity<b> </b>- A summary of key findings
Bibliographic record
Abstract
This document summarizes an exploratory qualitative interview study of diverse disability identities in Canada (Soldatic, Melbøe, Landry, and Novais 2025). Guided by the primary research question: How do people understand their disabilities in the context of their diverse lives across gender, sexuality, race, ethnicity and Indigeneity?, this study aims to centre diverse experiences of disability across a multiplicity of identities, to contribute to a growing field of research in Canada and an improved overall understanding of how people experience disability. Methodologically, the research has been guided by critical disability studies (CDS) and intersectionality. Interview data was analyzed using a constructivist grounded theory approach (Charmaz 2000). Cultural navigation is the theoretical framework developed to articulate the incredible amounts of labour participants do across a range of social, cultural, work and familial places and in everyday life, in navigating the cultural dominance of ableism, racism, settler colonialism, and other interlocking systems of oppression. This research is part of a comparative country study involving Norway (UiT Norges Arktiske Universitet) and Canada (TMU), to compare across nation-states with socio-political, geo-spatial and institutional similarities. Note that this summary document is presenting results from the Canadian study only.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".