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Record W4281570198 · doi:10.16995/labphon.6461

Production and perception across three Hong Kong Cantonese consonant mergers: Community- and individual-level perspectives

2022· article· en· W4281570198 on OpenAlexaff
Lauretta S. P. Cheng, Molly Babel, Yao Yao

Bibliographic record

VenueLaboratory Phonology Journal of the Association for Laboratory Phonology · 2022
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of British Columbia
FundersHong Kong Polytechnic University
KeywordsPerceptionPsychologyProduction (economics)Variation (astronomy)Context (archaeology)ConsonantGeneralizability theoryTask (project management)Developmental psychologyGeographySpeech recognitionEconomicsComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.332
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2022
Admission routes1
Has abstractyes

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