A prospectus for pronunciation research in the 21st century
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This inaugural issue of the Journal of Second Language Pronunciation, an auspicious step forward in our field, gives us an opportunity to take stock of current trends in pronunciation research with an eye to the future of this evolving field. As longtime researchers, we have learned many lessons by trial and error and wish to share our perspectives on sound methodological practices and on pitfalls to avoid. Our review follows the outline of a traditional experimental investigation, starting with the conceptualization of pronunciation research studies. We then discuss theoretical motivations, choice of constructs, and issues arising from the literature review. Next we compare several research designs and summarize types of data commonly used in pronunciation research. We then move on to consider data collection and analysis, focusing on reliability, effect sizes, and speaker variability, and to offer some caveats regarding the interpretation of results. We conclude by suggesting areas for future second language speech research, in terms of both replications and new studies.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it