The Identification of English Consonants by Native Speakers of Italian
Bibliographic record
Abstract
This study examined the identification of English consonants in noise by native speakers of Italian. The effect of age of first exposure to English was evaluated by comparing three groups of subjects who continued to use Italian relatively often but differed according to their age of arrival (AOA) in Canada from Italy (early: 7, mid: 14, late: 19 years). The subjects in the late group made more errors identifying word-initial consonants than subjects in the early group did; however, the effect of AOA was nonsignificant for word-final stops. The effect of amount of native language (L1) use was evaluated by comparing two groups of early bilinguals who were matched for AOA (mean = 7 years) but differed according to self-reported percentage use of Italian (early: 32%, early-low: 8%). The early bilinguals who used Italian often (early) made significantly more errors identifying word-initial and word-final consonants than native English (NE) subjects did, whereas the early bilinguals who used Italian seldom (early-low) did not differ from the NE subjects. The subjects' phonological short-term memory was estimated by having them repeat Italian non-words. This was done in an attempt to identify the source of individual differences. The nonword repetition scores were in fact found to independently account for 15% of the variance in subjects' errors identifying word-final English consonants and 8% of the variance for word-initial consonants.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".