Sociophonetic Variation and Change in Heritage Languages: Lexical Effects in Heritage Italian Aspiration of Voiceless Stops
Bibliographic record
Abstract
In a previous study on voiceless stop aspiration in Heritage Calabrian Italian spoken in Toronto, we found that the transmission of a sociophonetic variable differed from cross-generational phonetic variation induced by increased contact with the majority language. Universal phonetic factors and the social characteristics of the speakers appeared to influence contact-induced variation much more straightforwardly than the transmission of the sociophonetic variable. In the current study, we investigate further, examining possible alternative explanations related to the lexical distribution of the aspiration phenomena. We test two alternative hypotheses, the first one predicting that the diffusion of a majority language's phonetic feature is frequency-driven while change in a sociophonetic feature is not (or not that regularly across generations), and the second one predicting that sociophonetic aspiration decreases across generations by being progressively more dependent on the frequency of lexical items. Our results show that sociophonetic aspiration resists lexicalization and applies to both frequent and infrequent words even in the speech of third-generation speakers. By contrast, the progressive introduction of contact-induced phonetic change is led by high-frequency words. These findings add to the complexity of heritage language phonology by suggesting that the pronunciation features of a heritage language can follow different fates depending on their sociolinguistic roles.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".