The Conundrum of Training and Capacity Building for People with Learning Disabilities Doing Research
Bibliographic record
Abstract
BACKGROUND: This study explores the training involved when people with learning disabilities take their place in the community as researchers. This was a theme in a recent UK seminar series where a network of researchers explored pushing the boundaries of participatory research. METHOD: Academics, researchers with learning disabilities, supporters and other inclusive researchers considered important themes arising from presentations about developments in participatory research. The paper emerges from critical reflection on these rich discussions. RESULTS: A seminar series is a form of research training and capacity building, albeit a dynamic, interactive and collegial one. More formal training in research skills for people with learning disabilities is being developed but raises questions regarding the best contribution people with learning disabilities can make to the research process. CONCLUSION: There are various models of training for inclusive research, but these need to be reciprocal if they are not to undermine the inclusive goal.
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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 | 0.157 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.141 |
| Scholarly communication | 0.022 | 0.031 |
| Open science | 0.007 | 0.044 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".