An Investigation of Language-Specific and Orthographic Effects in L2 Arabic geminate production by Advanced Japanese- and English-speaking learners
Bibliographic record
Abstract
Research has indicated that second-language learners have difficulty producing geminates accurately. Previous studies have also shown an effect of orthography on second-language speech production. We tested whether the existence of a contrast in the first language phonology for length aids the second-language production of the same contrast. Furthermore, we examined the effect of exposure to orthographic input on geminate consonant production in a cross-script context. We tested the production of Arabic geminate-singleton stop consonants [/bː/-/b/, /tː/-/t/, /dː/-/d/, and /kː/-/k/], a nasal stop consonant /mː/-/m/, and an emphatic stop consonant /tˤː/-/tˤ/, as well as the effect of the diacritic used in Arabic to mark gemination in a delayed imitation task and two reading tasks (ortho-with diacritics and ortho-without diacritics). A comparison of the productions of advanced Japanese-speaking learners, English-speaking learners, and an Arabic control group showed that both learner groups were able to produce Arabic geminate stops; however, the Japanese-speaking learners exhibited an advantage over the English-speaking learners in the auditory-only task and in the presence of diacritics, highlighting the fact that orthographic effects may occur in some cross-script contexts.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".