Bibliographic record
Abstract
Abstract This paper presents the first sociophonetic study of Cantonese vowels using sociolinguistic interview data from the Heritage Language Variation and Change in Toronto Corpus. It focuses on four allophones [iː], [ɪk/ɪŋ], [uː], and [ʊk/ʊŋ] of two contrastive vowels /iː/ and /uː/ across two generations of speakers. The F1 and F2 of 30 vowel tokens were analyzed for these four allophones from each of 20 speakers ( N = 600 vowel tokens). Results show inter-generational maintenance of allophonic conditioning for /iː/ and /uː/ as well as an interaction between generation and sex such that second-generation female speakers have the most retracted variants of [ɪk/ɪŋ] and the most fronted variants of [iː]. This paper will discuss three possible explanations based on internal motivation, phonetic assimilation, and phonological influence. This will illustrate the importance of multiple comparisons (including inter-generational, cross-linguistic, and cross-community) in the relatively new field of heritage language phonology research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".