When Good Intentions Backfire: University Research Ethics Review and the Intimate Lives of People Labeled with Intellectual Disabilities
Bibliographic record
Abstract
We critically discuss how practices of ethical governance through university research ethics committees can contribute to the silencing of people labeled with intellectual disabilities through the reproduction of discourses of vulnerability and protectionism. In addition, disabling assumptions of (in)ability and reductive bio-medical understandings of labeled people as a homogeneous group can create concern that such research is "too risky," and perhaps not valuable enough to outweigh potential risks. Combined, these practices deem people "too vulnerable" or "too naïve," and thus, unable to make decisions for themselves about participating in research without putting themselves and the researcher(s) at risk. In this article, we draw on insights gained from our experiences undergoing ethics review for projects focused on the personal and intimate lives of people with intellectual disabilities. We proffer that such ethical governance, though well-intentioned (i.e., to protect participants and researchers), limits not only possibilities for research that would otherwise prioritize the perspectives and agency of people with intellectual disabilities but also how researchers are "allowed" to engage with them in research.
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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 | 0.460 | 0.512 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.034 | 0.088 |
| Scholarly communication | 0.037 | 0.025 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".