Developmental Sequences in Second Language Phonology: Effects of Instruction on the Acquisition of Foreign sC Onsets
Bibliographic record
Abstract
This study investigates the effects of three types of instruction on the acquisition of foreign /s/-initial onset clusters (/sl/, /sn/, and /st/-sC clusters), a process characterized by a developmental sequence in which /sl/ is acquired before /sn/ and /st/. 118 native speakers of Brazilian Portuguese participated in a 4-week language course to learn a set of vocabulary and associated pronunciation of Taki, a self-constructed miniature linguistic system. The participants were divided into three groups, each corresponding to one of the hypotheses that characterize three types of explicit second language instruction: Teachability, Projection of Markedness (Projection), and a combination of the two (Mixed). A mixed analysis of variance (ANOVA) analysis of the participants’ production of sC clusters revealed that, in one of the tasks employed (read aloud), the group that focused exclusively on the more marked/st/ (Projection Group) had the best overall performance in the acquisition of the three clusters: this group was able to generalize their knowledge to the sC clusters they were not taught. In general, the results support Zobl’s Projection Model of Markedness for explaining the phonological development of syllable structure, wherein the instructional effect of a focus on the most marked /st/ projects to knowledge of the less marked structures.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".