Integrating Visual Methods With Dialogical Interviews in Research With Youth Who Use Augmentative and Alternative Communication
Bibliographic record
Abstract
Scant information is available to guide the selection and modification of methods for doing research with people with communication impairments. In this article, we describe and illustrate a novel combination of methods used to optimize data generation in research with 13 disabled youth who use augmentative and alternative communication (AAC). Using a critical dialogical methodology developed for the study, we explored links between dominant calls for social inclusion, disabled youths’ social relations and life circumstances, and their position-takings in relation to inclusion. Building on emergent methodologies, we selected and integrated complementary methods: photo-elicitation, a graphic elicitation method termed “Belonging Circles,” observations, and interviews. The interview methods were modified to recognize all AAC modes used by participants and to acknowledge the relational, situated and thus, dialogical nature of all communication in interviews. Each method is described, and rationales for their selection and modification are discussed. Processes used to combine the methods, generate data, and guide analysis are illustrated using a case example from the study. The integrated methods helped illuminate the lives and practices of youth who use AAC and the strategies they used to negotiate inclusion across the social spaces that they traversed. We conclude with reflections on the strengths and limitations of our approach, future directions for development of the methodology, and its potential use in research with a broad range of persons experiencing communication impairments.
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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 | 0.198 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".