Bibliographic record
Abstract
■ Pronunciation is perhaps the linguistic feature most open to judgment.As a surface structure phenomenon that is most noticeable, one's accent easily evokes people's biases.For the same reason, pronunciation has been the most prescriptively taught aspect of language instruction.Pedagogies for accent reduction have bordered on the pathological.The articles in this special topic issue bring a much needed research focus on social and communicative considerations in pronunciation that can lead pedagogy in constructive new directions.Relating pronunciation to issues of identity, group membership, interpersonal negotiation, and the plurality of World Englishes, they treat the topic with great intellectual rigor.John Levis's editorial introduction and the article by Tracey M. Derwing and Murray J. Munro in the opening section discuss the importance of developing a research-based approach to pronunciation and chart the paradigm shift taking place in the field.In the next section, John Field and David Deterding shift the focus from the speaker to the listener as they explore the ramifications of negotiating intelligibility.The reality of World Englishes raises new questions for pronunciation in the third section, where the authors argue that "deviant" accents should be treated as legitimized in other speech communities.This does not mean, however, that speakers of various institutionalized local Englishes do not experience conflicts over which accent is preferable.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".