Articulatory Methods for the Study of Second Language Speech
Bibliographic record
Abstract
In the introduction to this special issue on articulatory approaches to the study of L2 speech, we first highlight the interest and unique contributions of such methods to the investigation of speech production among second language speakers. This is followed by a brief overview of the four articulatory methods-electropalatography, nasometry, magnetic resonance imaging, and ultrasound-featured in the experimental studies presented in the seven articles that constitute the issue. We then turn to an overview of the speech phenomena investigated-consonants (laterals, rhotics), vowels (individual as well as entire inventories), and sequences (both phonemic vowel-nasal sequences as well as coarticulation in phonetic sequences)-as produced by L1 speakers of various target languages (L1s: English[-Croatian], Czech, French, Japanese, Mandarin, Spanish; Target languages: English, French, Swedish). This introduction concludes with a summary of recurring acquisition themes (L1-based crosslinguistic influence, relative difficulty and target-likeness, inter-learner variability including as conditioned by individual differences) and the general speech phenomena studied (articulatory settings, gestural timing/coarticulation, effects of phonetic context).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".