A Collaborative Action Research about Making Self-Advocacy Videos with People with Intellectual Disabilities
Bibliographic record
Abstract
This article presents the results of a collaborative action research conducted with people living with intellectual disabilities (ID) who were going through a community integration process. To be successfully integrated into a community, they need to develop basic life skills as much as they need to learn to use mobile technologies for authentic interactions (Davidson, 2012) and to be self-advocates online (Davidson, 2009a). This study used the Capability Approach pioneered by Sen (1992) and Nussbaum (2000), which focusses on what people can do rather than on their deficiencies. I recruited a group of eight people with ID who wished to set goals, engage in developing new capabilities, share their goals and act as models for others with ID who want to learn to live on their own. In this article, I examine the process of developing self-advocacy videos with mobile technologies using the Capability Approach and I analyze the inventory of capabilities collected through this study. I provide recommendations for intervention through mobile technologies with the long term-goal of helping people with ID to become contributing citizens. I discuss the innovative action research methodology I used to help people with ID become self-advocates and take control of the messages they give through producing their own digital resources.
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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 | 0.023 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".