Putting accent in its place: Rethinking obstacles to communication
Bibliographic record
Abstract
One of the most salient aspects of speech is accent – either dialectal differences attributable to region or class, or phonological variations resulting from L1 influence on the L2. Our primary concern is with the latter, because of the strong social, psychological, and communicative consequences of speaking with an L2 accent. The decline of audiolingualism led to a concomitant marginalization of pronunciation research and teaching. It was believed that pronunciation instruction could not be effective, in part because of the unrealistic goal of native-like speech in L2 learners, and also because of research findings that suggested that instruction had a negligible impact on oral production. The recent revival of interest in pronunciation research has brought a change of focus away from native-like models toward easy intelligibility. The effects of this change have yet to be fully realized in L2 classrooms. However, many L2 students themselves are keenly interested in pronunciation instruction, a fact not lost on individuals who have recognized a lucrative marketing niche in ‘accent reduction/elimination’ programs that may do more harm than good. Our presentation will relate the core issues of intelligibility, identity, social evaluation, and discrimination to appropriate pronunciation pedagogy for L2 learners.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".