Bibliographic record
Abstract
Abstract This chapter discusses research methods in the domain of instructed second language (L2) pronunciation. After explaining what pronunciation is and why it is important, the chapter provides an overview of the current consensus surrounding instructed L2 pronunciation research, including major linguistic targets, speech modalities, and instructional techniques in both classroom- and laboratory-based studies, in addition to their effects on the acquisition of L2 pronunciation. It then introduces a range of speech elicitation instruments used in instructed L2 pronunciation research, followed by a discussion of approaches to L2 speech data analysis. The chapter also proposes future directions in instructed L2 pronunciation research and multiple venues in which L2 pronunciation researchers can disseminate their findings, while urging researcher-practitioner collaboration for evidence-based L2 pronunciation teaching. Finally, it concludes by shedding light on the importance of methodological rigor and offering troubleshooting strategies in instructed L2 pronunciation research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 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; both teacher heads agree on what is shown here.
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".