Childhood language exposure: Does early experience affect sound perception and production in speakers with reduced language proficiency?
Bibliographic record
Abstract
Early language exposure is crucial for acquiring native mastery of the phonology of a language (Fromkin 1974, Flege 1987, Antoniou et al. 2015). It is not clear, however, whether early language exposure has lasting benefits when the quantity and quality of speaking drop dramatically after childhood (Oh et al. 2003). In this study, we investigate the production of Arabic speech sounds in 20 native speakers (who heard and spoke Arabic during childhood regularly), 20 childhood speakers (who heard Arabic regularly during childhood but did not speak it regularly afterwards), and 20 novice speakers (did not have any exposure to Arabic during childhood but are currently enrolled in Arabic language classes). The experiment includes a language proficiency test and a phoneme production and perception task. The target sounds are geminate consonants. The acoustic analysis consists of manual alignment of each consonant, and extracting their duration and acoustic information (voicing, aspiration, and formant values). The findings shed more light on whether early language experience has measurable long-term benefits for an individual's phonetic and phonological skill even if the language experience diminishes over time. In addition, we gain further insight into the role played by universal markedness factors in L2 acquisition (Davidson, 2011).
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".