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The Short-a Nasal System: how does it get so tense?

2014· article· en· W2215143338 on OpenAlexaffabout
Paul De Decker

Bibliographic record

VenueOpen MIND · 2014
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsNasalityNasalizationVowelConsonantNasal vowelMid vowelPsychologyAudiologyRelative articulationArticulation (sociology)LinguisticsFormantMedicine

Abstract

fetched live from OpenAlex

Presented at the Canadian Linguistics Association annual meeting, Brock University, 2014. This paper investigates the production of the low front vowel /æ/ which undergoes “tensing” and “raising” (Ferguson, 1972; Labov, 1989) when followed by a nasal consonant in some dialects of English (Labov, Ash and Boberg 2006). Speakers in their early twenties, and life long residents of the province of Newfoundland read a word list which included five tokens of both hand and hat. All speakers exhibited significantly lower F1 and higher F2 values for the vowel in the word hand compared to hat. As a way to explain the acoustic differences found in nasal systems, we test the model outlined by Krakow et al. (1988), that such lowering and raising is a result of co-articulation between the vowel and the following nasal consonant.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.060
GPT teacher head0.377
Teacher spread0.317 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2014
Admission routes2
Has abstractyes

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