“I’ll Show You!”: Reflections on Disabled Children’s Points of View and the Use of Action Cameras in Inclusive, Critical, Qualitative Research
Bibliographic record
Abstract
Disabled children are often excluded from research about their own lives due to researchers’ reliance on inaccessible methods and ableist assumptions about their capabilities. Inclusive research approaches that prioritize accessibility, meaningful engagement, dignity, and ethics create space for research with and by disabled children. Accessible methods for data generation play a central role in inclusive research. For example, action cameras have the potential to shine light on disabled children’s perspectives from both socio-spatial and socio-cultural standpoints, providing unique access to disabled children’s points of view. While the use of action cameras has become increasingly popular across the social sciences, there is a paucity of literature that discusses the use of action cameras in data generation with disabled children. The purpose of this paper is to discuss the possibilities and challenges of using action cameras as a tool for data generation with disabled children. Drawing on lessons learned during a research project about disabled children’s and their families’ experiences of inclusion at a playground labelled as “inclusive”, we reflect on the use of action cameras and situate our experiences within a broader dialogue about reflexive, methodological, and ethical considerations for conducting inclusive, critical, qualitative research with disabled children.
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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 | 0.099 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.044 | 0.097 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.006 | 0.031 |
| Research integrity | 0.010 | 0.022 |
| 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".