Whose Research Is It? Reflection on Participatory Research with Women and Girls with Disabilities in the Global South
Bibliographic record
Abstract
Drawing on the Transforming Disability Knowledge, Research, and Activism project, funded by the Social Sciences and Humanities Research Council of Canada (2016-2020), this article critically reflects on the project’s participatory research process that involved young women and girls with disabilities in the Global South. I discuss epistemological and methodological questions related to the deployment of decolonizing research methodologies in the Global South in relation to theoretical and methodological approaches for engaging girls with disabilities. I argue that a critical, reflexive, and decolonizing research approach that embodies knowledge from the Global South is essential for empowering these girls to express themselves through multiple forms of representation.
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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 | 0.086 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.102 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".