Narrating Developmental Disability: Researchers, Advocates, and the Creation of an Interview Space in the Context of University-Community Partnerships
Bibliographic record
Abstract
This paper examines the narration of developmental disability through interviews between participants, researchers, and members of community organizations serving the disabled population, in the context of university-community collaborations. These kinds of collaborations are extremely important for researching vulnerable or hard-to-reach populations, which often face lower levels of physical, mental, and social well-being as a consequence of shame, stigma, or discrimination. Community collaboration can thus be invaluable for reaching members of marginalized populations, who may be difficult to locate or otherwise avoid contact with outsiders, because it provides members of a research team with local knowledge of a population, a means of accessing possible participants, and legitimation for the project. I suggest, however, that although the researcher's externality may initially invite skepticism toward the investigation from participants, it can also benefit them by providing a forum for catharsis. Based on a pilot study I conducted with a community advocacy organization for the disabled, I note that some participants expressed an appreciation for being able to discuss certain emotions and experiences during interviews with an outsider who was not involved as a caseworker. I conclude that the presence of a trusted community advocate and a researcher at an interview affects a participant's narrative by providing a safe space for participants to voice their stories to outsiders.
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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 | 0.056 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.044 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.007 | 0.008 |
| 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".