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Record W2467360993 · doi:10.3765/amp.v3i0.3678

Extrametricality and second language acquisition

2016· article· en· W2467360993 on OpenAlexaff
Guilherme D. Garcia

Bibliographic record

VenueProceedings of the Annual Meetings on Phonology · 2016
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsReset (finance)Stress (linguistics)Task (project management)LinguisticsWord (group theory)PsychologyFoot (prosody)JudgementComputer sciencePortugueseCognitive psychologyNatural language processingSpeech recognition

Abstract

fetched live from OpenAlex

This paper examines how native English speakers acquire stress in Portuguese. Native speakers and second language learners (L2ers) of any given language have to formulate word-level prosodic generalizations based on a subset of lexical items to which they have been exposed. This subset contains robust as well as subtle cues as to which stress patterns are more or less productive, so that when speakers encounter novel forms they know which stress position is more likely. L2ers, however, face a much more challenging task, mainly if they are adults and have long passed the critical period. These difficulties are particularly notable in word-level prominence, where several interacting phonetic cues are involved. The trends observed across three proficiency levels in the judgement task described in this paper are consistent with a foot-based analysis, and show that L2ers successfully reset extrametricality (Yes in the L1; No in the L2) and shift the default stress position from antepenult (L1) to penult (L2). The latter is expected to follow from the former in a foot-based approach where feet become aligned to the right edge of the word as extrametricality is reset to No.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.823

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 science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.298
Teacher spread0.284 · 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 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

Citations4
Published2016
Admission routes1
Has abstractyes

Explore more

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