Bibliographic record
Abstract
The phenomenon referred to as 懶 音 laan5 jam1, or “lazy pronunciation”, in Hong Kong Cantonese (HKC) is a set of consonant mergers-in-progress that have been studied for many decades; these involve consonants in syllable-initial, syllable-final and syllabic positions (eg. Wong, 1941; Zee, 1999). A recent apparent time production study in Hong Kong reported that several of the syllable-initial mergers were nearing completion in the youngest generation (To, Mcleod & Cheung, 2015). While this sound change has been well-documented within Hong Kong, only a limited number of studies have examined Cantonese phonology in immigrant communities (e.g. Tse, 2016), and none appear to have targeted the consonant mergers. As such, the current study investigates both perception and production of a subset of the HKC mergers ([n-]→[l-], [ŋ-]↔Ø-, [ŋ̩]→[m̩]) in Vancouver’s immigrant Cantonese-speaking population, comparing across older and younger generations as well as to speakers in Hong Kong. The perception experiment used a two-alternative forced-choice lexical identification task. Participants heard Cantonese words from 13-step minimal word-pair continua ranging from the innovative to conservative variant for each merger, and their task was to indicate which lexical item they heard. The production experiment was an isolated-word production task. Participants were prompted with both Chinese characters and the English translation to produce 22 Cantonese words containing the target contrasts. Finally, speaker awareness of the mergers was probed in a post-task interview, and bilingual dominance scores were calculated using the Bilingual Language Profile (Birdsong, Gertken & Amengual, 2012). The results of this study add a new perspective to scholarship on the HKC mergers and on the course of sound change in immigrant communities more generally, while also contributing to research on the phonetics and phonology of Cantonese heritage speakers using an experimental production and perception approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".