Empowerment in decision-making for autistic people in research
Bibliographic record
Abstract
Empowerment in research is important in many autism and autistic communities and an important part of ‘nothing about us without us’. It is also an important component of person-oriented research ethics. This article reviews the literature on ethics in autism research for information related to decision-making empowerment for autistic people. A review of 81 articles reveals several themes and specific strategies. Empowerment is important for, but also goes beyond, establishing informed consent. Empowerment is a form of participant and community engagement, and necessarily shaped by specific context. The view of research ethics put forth in this article envisions ethics as a potential avenue for empowerment, where research participants are able to decide how to be involved and to shape research processes and contexts. This view of research ethics is aligned with the aspirations of many in advocacy communities, though it may not correspond to conventional understandings of research ethics.Points of interestThis article talks about ethics in autism research.It focuses on the importance of people with autism having the power to make choices about research.It describes what published articles have said about this issue.Making choices about research includes not only the choice to take part in a study or not, but also many other choices before, during, and after the study.The way that this article talks about research ethics helps achieve goals of many autistic people and disabled people to be included.
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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 | 0.112 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.065 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.005 | 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".