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Record W4298210853 · doi:10.1558/jmbs.20388

Redeploying appendices in L2 phonology

2022· article· en· W4298210853 on OpenAlexaff
John Archibald, Marziyeh Yousef, Amjad Alhemaid

Bibliographic record

VenueJournal of Monolingual and Bilingual Speech · 2022
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPerceptionPhonologyLinguisticsObstruentSyllabic verseLicensePersianPsychologyConsonantVowelComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

In this paper, we explore aspects of the production and perception of certain consonant clusters (in particular s + C clusters) in second language learners. We administered perception tasks (ABX and non-word transcription) and production tasks (reading, picture-based discussion, and elicited imitation) to native speakers of Persian and Arabic, and compare their results to those in previously published studies of other L1s. We will arrive at two broad conclusions. The first is that many subjects who demonstrate non-targetlike production of consonantal sequences by producing epenthetic vowels between the consonants are not hearing an illusory vowel in perception tasks. Thus, non-nativelike production is not always reflective of non-nativelike perception; non-nativelike production is not always caused by non-nativelike perception. Our second conclusion is that the locus of explanation for the accurate perception in subjects whose L1s lack s + C clusters is the presence or absence in the L1 of right-edge syllabic appendices. L1s which do not license appendices (Japanese, Brazilian Portuguese) will have difficulty perceiving L2 English s + C sequences, while L1s which do license appendices (Persian, Arabic) will not have difficulty perceiving L2 English s + C strings.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.345
Teacher spread0.316 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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