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Record W2049459113 · doi:10.1121/1.3508023

A test for errors of phonological rule processing.

2010· article· en· W2049459113 on OpenAlexaff
Andrea Gormley, John S. Logan

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2010
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsCarleton University
Fundersnot available
KeywordsSchwaPhonological ruleComputer scienceCognitionSpeech recognitionRange (aeronautics)Set (abstract data type)Process (computing)PhonologyPsychologyLinguisticsVowelEngineering

Abstract

fetched live from OpenAlex

As a cognitive process, phonological alternations should be subject to error under high cognitive load. An experiment was designed to determine if phonological processes err by comparing two sets of tokens; one that contained a rule and a second set, matched in form, that did not. Samples were obtained from 16 speakers for two types of stimuli: one targeting the phonological process of flapping and the other targeting the phonological process of schwa-insertion. Acoustic analyses were performed to determine the normal range for each speaker. Tokens that fell two standard deviations outside of this range were counted as errors. The frequency of errors was compared to see if the phonological rule condition had more errors. The forms with flapping and schwa-insertion did not induce more errors than the comparison forms without these processes. The results do not provide sufficient evidence for the existence of rule processing errors. This result calls into question the status of these phonological alternations as cognitive processes.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.029
GPT teacher head0.344
Teacher spread0.315 · 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 designBench or experimental
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
Published2010
Admission routes1
Has abstractyes

Explore more

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