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Cue reweighting in Shanghainese sandhi patterns

2022· article· en· W4327520891 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueExLing Conferences · 2022
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsVoiceDuration (music)Tone (literature)Contrast (vision)SyllableSpeech recognitionComputer scienceRegister (sociolinguistics)Closure (psychology)LinguisticsArtificial intelligenceAcoustics

Abstract

fetched live from OpenAlex

The role of cue reweighting in reshaping tone sandhi patterns has rarely been discussed. The prevailing view is that, in Shanghainese disyllabic prosodic words, F0 distinctions between lexical tones are neutralised in the second syllable, while voice onset time (VOT) and closure duration function as the primary cues maintaining the voicing/register contrast. However, this study argues that a cue reweighting process is currently reshaping Shanghainese sandhi patterns: F0 is beginning to replace VOT and closure duration as the primary cue for voicing/register contrast in the second syllable position. This gradual acquisition of primary cue status by F0 in sandhi tones closely resembles processes of tone split observed in initial positions in other languages.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.000

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 head0.063
GPT teacher head0.366
Teacher spread0.303 · 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