Global Accent in the Portuguese Speech of Heritage Returnees
Bibliographic record
Abstract
The present study examined whether heritage speakers (HSs) of European Portuguese (EP) who were born or moved to a German-speaking country before the age of eight years were perceived as native speakers of EP. In particular, this study intended to determine whether a change of linguistic environment, length of residence in a migrant context, length of residence in the country of origin before migration and after remigration, and age at return could predict the degree of (non)native accent in the heritage language. Thirty native Portuguese speakers assessed the global accent of 20 Portuguese-German bilinguals, five Portuguese monolinguals and five highly proficient German speakers of Portuguese as a second language (L2). The group of HSs comprised 17 speakers who returned to Portugal. The results revealed that listeners perceived a strong global foreign accent in the speech of the L2 learners, while the monolingual Portuguese speakers were clearly perceived as being native speakers of EP. The HSs’ ratings were considerably closer to the monolingual average ratings, but they showed more variation, indicating that their accent may bear non-native traces. Further analyses showed that the age at which the HSs emigrated was the only significant predictor, while length of residence in the host country and in Portugal were less predictive.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".