Redeploying appendices in L2 phonology
Bibliographic record
Abstract
In this paper, we explore aspects of the production and perception of certain consonant clusters (in particular s + C clusters) in second language learners. We administered perception tasks (ABX and non-word transcription) and production tasks (reading, picture-based discussion, and elicited imitation) to native speakers of Persian and Arabic, and compare their results to those in previously published studies of other L1s. We will arrive at two broad conclusions. The first is that many subjects who demonstrate non-targetlike production of consonantal sequences by producing epenthetic vowels between the consonants are not hearing an illusory vowel in perception tasks. Thus, non-nativelike production is not always reflective of non-nativelike perception; non-nativelike production is not always caused by non-nativelike perception. Our second conclusion is that the locus of explanation for the accurate perception in subjects whose L1s lack s + C clusters is the presence or absence in the L1 of right-edge syllabic appendices. L1s which do not license appendices (Japanese, Brazilian Portuguese) will have difficulty perceiving L2 English s + C sequences, while L1s which do license appendices (Persian, Arabic) will not have difficulty perceiving L2 English s + C strings.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".