Communication Problems Between Researchers and Informants With Speech Difficulties: Methodological and Analytic Issues
Bibliographic record
Abstract
Using data collected in a study of how people living with Parkinson's disease assess the efficacy of the alternative and complementary therapies they use, the author addresses the impact on qualitative data collection and analysis of communication problems between researchers and informants who experience speech difficulty. There is little literature that deals with these issues. In what little does, the emphasis is most often on the “problem” informants who experience speech difficulty present for communication, rather than seeing communication problems as a product of interviewer/informant interaction in which the researcher also plays a role in hindering communication. In this article, the author argues that a focus on the linguistic inability of informants, however unwitting, constructs the person who experiences difficulty speaking as problematic and the researcher as the “expert” who solves the communication problem the informant presents.
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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 | 0.554 | 0.631 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.018 | 0.036 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.008 | 0.007 |
| 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".