A Viewpoint on Accent Services: Framing and Terminology Matter
Bibliographic record
Abstract
PURPOSE: The purpose of this article is to offer a contemporary viewpoint on accent services and contend that an equity-minded reframing of accent services in speech-language pathology is long overdue. Such reframing should address directly the use of nonpejorative terminology and the need for nurturing global linguistic diversity and practitioner diversity in speech-language pathology. The authors offer their perspective on affirmative and least-biased accent services, an in-depth scoping review of the literature on accent modification, and discuss using terms that communicate unconditional respect for speaker identity and an understanding of the impact of accent services on accented speakers. CONCLUSIONS: Given ongoing discussions about the urgent need to diversify the profession of speech-language pathology, critical attention is needed toward existing biases toward accented speakers and how such biases manifest in the way that accent services are provided as well as in how clinicians conceptualize their role in working with accented speakers. The authors conclude with discussing alternate terms and offer recommendations for accent services provided by speech-language pathologists.
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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 | 0.049 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.057 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 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".