Production and perception across three Hong Kong Cantonese consonant mergers: Community- and individual-level perspectives
Bibliographic record
Abstract
Individual variation is key to understanding phenomena in phonetic variation and change, including the production-perception link. To test the generalizability of this relationship, this study compares community- and individual-level variation across three long-standing consonant mergers in Hong Kong Cantonese speakers: [n]→[l], [ŋ̩]→[m̩], and [ŋ]↔Ø. Concurrently, we document these understudied mergers in a community that has undergone rapid social change in recent decades. Younger (college-aged) and older (middle-aged) Hong Kongers completed a reading production task followed by a forced-choice lexical identification perception task. Group-level results suggest mismatching production and perception: While the community overall distinguished merger pairs in production, younger listeners are more perceptually categorical than older listeners. However, aggregate results obscure the fact that individuals vary substantially in the extent of merging in both perception and production, including many who exhibit complete merger, and that individual-level production-perception correlations were found for [n]→[l] and [ŋ̩]→[m̩], though not [ŋ]↔Ø. Results are discussed in the context of previous research. We find that (i) these mergers have diverged from predicted trajectories of completion, and (ii) overall, prior findings on the production-perception link are generalizable to these consonant mergers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".