Cognitive Accessibility, Ethics, and Rights in Research
Bibliographic record
Abstract
This paper is about doing research with artists with learning disabilities and autism. Artists with learning disabilities or autism need to say yes to doing research. Researchers need to explain what will happen in the research. Often, researchers think that people with learning disabilities and autism can’t say yes to being part of research. This means researchers don’t study things that are important to people with learning disabilities and autism. So, people with learning disabilities and autism don’t get a voice. All the people who wrote this work on I’m Me. I’m Me was a research project that works with seven learning disability arts companies in the United Kingdom. We used drama, dance, music and art to understand identity, representation, and voice. This paper explores our approach to ethics on the project. We wanted to avoid what we describe as a “deficit model” of ethics. Instead we worked with learning disabled artists and researchers to develop a set of rights in research. How these rights were communicated was very important in order to ensure access and understanding. I’m Me used a range of methods, including illustrations, workshops, videos, and movement. As a result of this approach, we found artists started independently talking about their rights in research. Plain text abstract (adapted by Kelsie Acton with Daniel Foulds) This paper is about doing research with artists with learning disabilities and autism. Artists with learning disabilities or autism need to say yes to doing research. So researchers need to explain what will happen in the research. Often, researchers think that people with learning disabilities and autism can’t say yes to being part of research. This means researchers don’t study things that are important to people with learning disabilities and autism. So, people with learning disabilities and autism don’t get a voice. All the people who wrote this work on I’m Me. I’m Me was a research project that works with seven learning disability arts companies in the United Kingdom. We used drama, dance, music and art to understand identity, representation, and voice. We wanted to make sure the artists could decide if they wanted to be a part of the research. To do this we assumed that people with learning disabilities and autism can be a part of research, assumed that people with learning disabilities and autism have rights in research, and did not assume that people with learning disabilities and autism need to be protected. Practically, we used plain language, pictures, We found: Making sure artists knew their rights took time. We needed to talk about artists’ rights several times. But artists understood their rights better when they talked about them more. Giving artists credit for their work is important. But making sure that people reading research don’t know who the research is about is also important. We found it hard to explain when artists’ names would and wouldn’t be used. We needed to experiment to find the best way. Learning how to give people with learning disabilities and autism the information they need to decide if they want to be part of research is important.
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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 | 0.084 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.192 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".