Articulatory settings and L2 English coronal consonants
Bibliographic record
Abstract
Abstract Background/aims: We explore the potential contribution of Articulatory Settings (AS) theory to L2 speech production research, testing the hypothesis that L2 segmental speech learning should involve a gradual, overall shift in both place and constriction degree, simultaneously affecting all consonants of a series as opposed to a set of parallel but unrelated changes in learners’ production of individual sounds.Methods:We conducted an electropalatography study of four francophone learners’ production of French and English word-initial and -medial /t d s z n l/ via carrier-sentence reading tasks.Results: L1–L2 differences in tongue shape are more common than those in constriction location, first and foremost for sonorants, and, thus, our results are not completely consistent with AS theory’s claims.Conclusions: AS theory provides a potentially rich framework for exploring the L2 speech learning of consonantal phenomena including low-level L1–L2 differences in place of articulation. We propose that the observed lack of systematic between-language articulatory differences could be attributed to a number of factors to be explored in future research, such as the targeting of voicing and manner differences before the adjustment of small place differences as well as individual patterns of entrenchment of L1 articulatory routines.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".