Bibliographic record
Abstract
Background: While it is recognised that conversation partners of people with aphasia often speak for them, investigation of “speaking‐for” incidents has shown that these comprise a wide range of behaviours, leading Simmons‐Mackie, Kingston, and Schulz (2004 Simmons‐Mackie, N., Kingston, D. and Schultz, M. 2004. “Speaking for another”: The management of participant frames in aphasia.. American Journal of Speech‐Language Pathology, 13: 114–127. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) to identify a “fine interactive line” (p. 123) between “speaking for” and “speaking instead of”. To date, however, there has been little exploration of these behaviours in the context of everyday family conversation; furthermore, little is known about how family members themselves interpret the actions of speaking for their relative with aphasia. Aims: The goal of this paper is to describe how the husband of a woman with progressive nonfluent aphasia (PNFA) and their adult children experienced and interpreted his ways of speaking for her. Methods & Procedures: Findings are drawn from a qualitative case study exploring a family's experiences of progressive aphasia through analyses of their talk. Methodology included a thematic analysis of in‐depth interviews conducted with each of six family members and conversation analysis of their everyday conversations together, selected and audio recorded by the participants themselves over a 3‐month period. Outcomes & Results: The husband's “speaking‐for” behaviours, which emerged as a significant theme in the interview data from him and all four adult children, were linked to long‐standing patterns of interaction but were described as problematic in the context of his wife's aphasia. Conversation analysis revealed that he used three patterns of “speaking‐for” behaviours, each with different interactional strategies and consequences. Conclusions: Discussion highlights the nuances, challenges, and complexities of “speaking for” behaviours when considered in the historical context of relationship.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.043 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".