MétaCan
Menu
← Back to cohort
Record W4205344503 · doi:10.31219/osf.io/892fp

Extrametricality and second language acquisition

2021· preprint· en· W4205344503 on OpenAlexaff
Guilherme D. Garcia

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsStress (linguistics)SyllableCategorical variableVariety (cybernetics)PsychologyProsodyLinguisticsFeature (linguistics)Second-language acquisitionEuropean PortuguesePortugueseComputer scienceCognitive psychologySpeech recognitionArtificial intelligence

Abstract

fetched live from OpenAlex

This pilot study investigates the second language acquisition (SLA) of stress in Portuguese (L2) by native speakers of English (L1). In particular, it examines the interaction between extrametricality and default stress through two judgement tasks. Stress is suprasegmental, relative and involves a variety of phonetic correlates: Cross-linguistically, stressed syllables tend to be realized with higher pitch, longer duration and greater intensity—but languages differ as to which of these correlates is more or less significant. Phonologically, stress presents some unique characteristics, such as the absence of a categorical feature [±stress]. Languages may also differ as to whether syllable shape affects stress (weight-sensitive) or not (weight-insensitive). Second language learners (L2ers) have to deal with such variability and, more importantly, have to acquire new stress patterns—some of which are often vastly different (even contradictory) when compared to the patterns (and phonetic cues) in their L1.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.042
GPT teacher head0.388
Teacher spread0.346 · 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 designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicPhonetics and Phonology Research→French-language works237,207→