MétaCan
Menu
Back to cohort
Record W4312856790 · doi:10.1121/10.0016215

An examination of the perceptions of Cuban and Peninsular Spanish varieties by native, second language learners of Spanish, and monolingual English speakers

2022· article· en· W4312856790 on OpenAlexaff
Gabriela Martinez Loyola, Yasaman Rafat

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSpanish Linguistics and Language Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsActive listeningStress (linguistics)First languagePsychologyCategorizationPerceptionVariety (cybernetics)Second languageLinguisticsCommunicationMathematics

Abstract

fetched live from OpenAlex

Individuals have been found to make judgments on a person’s personality, income, education, and employment in as little as 30 seconds after listening to their voice. This study investigates the perceptions of Cuban and Peninsular Spanish varieties by native Cuban and Peninsular Spanish speakers, second language (L2) Spanish learners, and monolingual English speakers, to analyze whether (i) these groups differ in their ability to recognize these varieties, and (ii) whether there is stigma attributed to either accent. The study consisted of 5 Cuban (Havana) and 5 Peninsular (Madrid) voices which were disguised and rated by 20 adult listeners. The methodology included the administration of a Bilingual Language Profile (BLP) questionnaire and a survey intended to gauge at listeners’ perceptions. Results revealed that listeners do make unconscious assumptions on an individual’s voice, as the Peninsular variety was often attributed to a higher educational level (84% SP, 48% CU), income (60% SP, 40% CU), and was more closely associated with a CEO position (32%), while the Cuban voice was associated with being funnier and slightly more intelligible. Furthermore, native Cuban listeners were found to outperform all other groups in their ability to correctly categorize the accents with an accuracy rate of 92%.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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 designQualitative
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicSpanish Linguistics and Language StudiesFrench-language works237,207