Intersections of literal and metaphorical voices in aphasia
Bibliographic record
Abstract
Background: “Voice” in the aphasia literature has come to include metaphorical meanings associated with social identity and inclusion. Concepts of metaphorical voice acknowledge communication as a primarily social act through which we construct our identities, prompting attention to how social practices can impact those identities, either supporting or silencing metaphorical voice. The impact of aphasia on literal voice, (i.e., the physical production of spoken language) is also acknowledged in the research literature, but the intersections of metaphorical and literal voice in aphasia have rarely been explicitly addressed. Aims: The aim of this paper is to foreground these intersections through a case study involving a novel application of the software program SentenceShaper®, which can be used to facilitate construction of messages recorded in one's own voice. Methods & Procedures: This qualitative case study describes a project involving a man with nonfluent aphasia and apraxia who worked with a graduate student clinician over several months using SentenceShaper® to record a specific text for a specific purpose. Interpretative description is used to analyse the process and product of their interactions, set within the philosophical framework of a social intervention model. Data sources include the recording itself, field notes, and written reflections on interaction. Outcomes & Results: Findings show how literal voice is linked to identity, creating an authentic link between person and message. They also highlight ways in which a social approach to therapeutic interaction can support both literal and metaphorical voice. Finally, they illustrate the creativity with which a person with aphasia integrates components of a therapeutic process into a repertoire of tools to support communication. Conclusions: Literal and metaphorical voice are inextricably linked. Considering voice in both senses has the potential for identifying goals that might otherwise be overlooked.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".