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Record W2485434458 · doi:10.1075/rllt.9.15maz

Age effects and the discrimination of consonantal and vocalic contrasts in heritage and native Spanish

2016· book-chapter· en· W2485434458 on OpenAlexaff
Natalia Mazzaro, Alejandro Cuza, Laura Colantoni

Bibliographic record

VenueRomance languages and linguistic theory · 2016
Typebook-chapter
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDiscrimination testingPsychologyLinguisticsAffect (linguistics)PerceptionSignificant differenceCommunicationMathematics

Abstract

fetched live from OpenAlex

This study explores the perception of consonantal and vocalic contrasts in two groups of Spanish-English bilingual speakers: heritage speakers and long-term immigrants. We test the discrimination of Spanish stops and mid and high vowels via an AX discrimination task with natural stimuli consisting of real Spanish words. Overall, results revealed no significant differences between heritage speakers and long-term immigrants in their discrimination of Spanish stops and vowels. Both groups were more accurate in their discrimination of vowels than of consonants. As for the discrimination of stops, positional and place effects were observed; i.e. a higher proportion of errors was found in word-initial position and with dorsals. We argue that contact with English does not necessarily affect the discrimination of the Spanish contrasts. Implications of these results for maturational approaches to final L2 attainment are discussed.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.302
Teacher spread0.293 · 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 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

Citations11
Published2016
Admission routes1
Has abstractyes

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