Symposium – Accentuating the Positive: Directions in Pronunciation Research
Bibliographic record
Abstract
Held at the Association canadienne de linguistique appliquée/Canadian Association of Applied Linguistics Conference, Ottawa, Canada; 27 May 2009. Over the past few decades perspectives on second language (L2) pronunciation have evolved from pessimistic appraisals of the capabilities of L2 learners and doubts about the value of instruction to a view of pronunciation teaching as an effective and important part of language pedagogy. Earlier research on the teaching of pronunciation dwelt extensively on the identification of learners' errors (mainly consonants and vowels) through comparative analyses. Until recently, little had been established about the effectiveness of pronunciation teaching, and pedagogical techniques were based more on speculation and theoretical notions than on empirically well-justified principles. More recent work addresses a broader range of issues relating not only to L2 phonological acquisition, but to the social implications of speaking with an accent and engaging with interlocutors, both native and non-native speakers of the L2. Among these are the relationship between accent and intelligibility, cognitive processes underlying phonological learning, the evaluation of L2 speech using impressionistic and acoustic techniques, prosodic influences on perception of accented speech, the role of ethnic affiliation and identity in L2 speakers' oral production, and the identification of misguided applications of knowledge about pronunciation by businesses and governments. These lines of work, along with empirical investigations of pronunciation instruction, engender a more sophisticated view of L2 phonological learning and teaching. Though further research remains to be done, important achievements have been made in identifying reasonable, achievable goals in the pronunciation classroom, establishing appropriate instructional foci, and evaluating outcomes. The presenters in this colloquium highlighted the major achievements of recent years and identified some of the important problems that remain.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".