‘Communicative competence’ in the field of augmentative and alternative communication: a review and critique
Bibliographic record
Abstract
BACKGROUND: Understandings of 'communicative competency' (CC) have an important influence on the ways that researchers and practitioners in augmentative and alternative communication (AAC) work toward achieving positive outcomes with AAC users. Yet, very little literature has critically examined conceptualizations of CC in AAC. Following an overview of the emergence of the concept of CC and of the field of AAC, we review seven conceptualizations of CC identified in the literature. AIMS: To consider the contributions and potential shortcomings of conceptualizations of CC in AAC. METHODS & PROCEDURES: We use a critical theoretical approach to review, critique and synthesize conceptualizations of CC in AAC, with a particular focus on uncovering 'taken for granted' assumptions. By historically situating the reviewed literature, we examine the shifting boundaries and tensions among theoretical conceptualizations of CC in AAC and their potential impacts on practice. MAIN CONTRIBUTIONS: We suggest ways that revisiting past scholarly work, alongside emergent, innovative conceptualizations of CC might shift ways of thinking about CC in AAC which tend to focus on the individual who communicates differently, toward (re)location of CC as a shared, socially incorporated and performed communication construct. CONCLUSION & IMPLICATIONS: We propose that emerging critical perspectives drawn from AAC and other interdisciplinary literatures offer innovative ways of theorizing communication difference, which might inform evolving conceptualizations of CC in AAC.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| 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; 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".