Perceptual categorization of English vowels by native European Portuguese speakers
Bibliographic record
Abstract
This study reports the results of a perceptual assimilation task (PAT) used to assess the degree of perceived cross-language (dis)similarity between the vowel inventories of European Portuguese (L1) and American English (L2) and, thus, predict difficulty in the perception and production of non-native vowels. Thirty-four native European Portuguese speakers completed a PAT, in which they mapped both L2 English and L1 Portuguese vowels to native vowel categories and rated them for goodness-of-fit to L1 vowels. The results are discussed in terms of theoretical models of cross-language perception and L2 speech learning (SLM, Flege, 1995, & PAM-L2, Best & Tyler, 2007).-----------------------------------------------------------------------------CATEGORIZAÇÃO PERCEPTIVA DE VOGAIS INGLESAS POR FALANTES NATIVOS DE PORTUGUÊS EUROPEUEste estudo reporta os resultados de uma tarefa de assimilação percetiva, usada para avaliar o grau de semelhança inter-linguística entre os inventários vocálicos de português europeu (L1) e de inglês americano (L2), e, assim, prever dificuldades na perceção e produção de sons não nativos. Trinta e quatro falantes nativos de português europeu completaram uma tarefa de assimilação perceptiva, na qual identificaram vogais do inglês (L2) e do português (L1) de acordo com as categorias fonológicas da sua língua nativa, avaliando também a qualidade de representatividade categorial. Os resultados são discutidos partindo de dois modelos de perceção inter-linguística e aprendizagem de fala L2 (SLM, Flege, 1995, & PAM-L2, Best & Tyler, 2007).---Original em inglês.
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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 | 0.005 |
| 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 | 0.001 |
| 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".