What does your accent say about you? The perception of Cuban and Peninsular Spanish varieties by native and non-native speakers of Spanish
Bibliographic record
Abstract
Abstract Individuals can make judgments on a person’s personality and socioeconomic status in as little as 30 s after hearing 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. Specifically, it analyzes whether (i) these speakers differ in their ability to recognize these varieties, and (ii) the perceptions of these groups differ to determine unconscious biases. Fifty adult listeners rated 5 Cuban (Havana) and 5 Peninsular (Madrid) disguised female voices. They completed a Bilingual Language Profile (BLP) questionnaire and a survey to examine unconscious accent categorization and perceptions. The results revealed that individuals do in fact make unconscious assumptions on an individual’s voice, as the Peninsular variety was often attributed to higher education and income levels and was closely associated with a higher rank (CEO) position compared to the Cuban variety on behalf of all groups. Furthermore, native Cuban listeners were found to outperform all groups in correct categorization of the accents heard. This study illustrates how perceptions toward stigmatized language varieties transcend native speakers of a language.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".