Persistent errors in the perception and production of word-initial English stop consonants by native speakers of Italian
Bibliographic record
Abstract
This study examined the perception and production of word-initial tokens of English stops (/b d g/ and /p t k/) by native speakers of Italian. The native Italian subjects were assigned to one of four groups based on their age of arrival (AOA) to Canada from Italy and percentage of self-reported use of the native language, Italian. The results obtained here suggested that AOA was a more important predictor of the native Italian subjects’ perception and production of word-initial English stops than L1 use was. The results also provided evidence of native versus non-native differences in segmental perception and production that persisted after decades of frequent second-language use. As hypothesized, the native Italian subjects produced English /p t k/ more accurately than /b d g/. In a perception experiment examining naturally produced English stops, the native Italian subjects misidentified short-lag tokens of English /b d g/ as /p t k/ more often than they misidentified long-lag /p t k/ tokens as /b d g/. Late bilinguals erred more often in identifying the voicing feature in /b d g/ than did early Italian–English bilinguals or native speakers of English, apparently because Italian /p t k/ are realized with short-lag VOT values.
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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 | 0.003 |
| 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 | 0.000 |
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".