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Record W2793450032 · doi:10.3765/amp.v5i0.4224

Does SAE have /flap/? Evidence from Canadian Raising and Vowel Durations

2018· article· en· W2793450032 on OpenAlexaboutno aff
Bethany Dickerson

Bibliographic record

VenueProceedings of the Annual Meetings on Phonology · 2018
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
FundersCollege of Engineering, Michigan State UniversityMichigan State University
KeywordsLexiconRaising (metalworking)VowelLinguisticsComputer scienceSpeech recognitionAcousticsMathematicsNatural language processingPhysicsPhilosophy

Abstract

fetched live from OpenAlex

In American English, /t/ and /d/ neutralize to flaps intervocalically: write ∼ writer and ride ∼ rider. There also exist words which contain ambiguous surface flaps that do not alternate, for example, the name Ryder. Does the language user treat this flap like an allophone of /t/ or /d/ (as predicted by the Free Ride Principle), or as an independent segment (as predicted by Lexicon Optimization)? To investigate this question, acoustic data from elicited (Experiment 1) and spontaneous (Experiment 2) speech are analyzed. Voiceless consonants cause the vowel preceding them to be shorter and trigger Canadian Raising. Therefore, if language users treat non-alternating flaps as allophones of /t/, the vowel durations and F1 trajectories of the vowels in these two environments will be similar to each other and different from before flapped /d/, and vice versa if language users treat non-alternating flaps as allophones of /d/. If language users treat non-alternating flaps as different than both the flap allophones of /t/ and /d/, vowel durations and F1 will be different in all three environments. Results show that the durations and F1 trajectories of the vowels in the three environments are all different from each other, providing evidence for Lexicon Optimization.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.320
Teacher spread0.290 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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