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Enregistrement W2289538568 · doi:10.14288/1.0078283

The role of linguistic context in interlanguage phonology

2010· article· en· W2289538568 sur OpenAlexaboutno aff
Guzide Dilek Cansin

Notice bibliographique

RevuecIRcle (University of British Columbia) · 2010
Typearticle
Langueen
DomainePsychology
ThématiqueSecond Language Acquisition and Learning
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLinguisticsInterlanguagePhonologyContext (archaeology)Linguistic contextLinguistic descriptionLinguistic analysisHistoryPhilosophy

Résumé

récupéré en direct d'OpenAlex

The phenomenon of the foreign accent has long been of interest to linguists, second language teachers, language pathologists, and others. This study investigated the influence of certain factors on the degree of foreign accent in learners of English as a second language. Specifically, it examined the effects of two linguistic contexts, age of arrival in Canada, years spent in Canada, and native language on the accents of 29 subjects at an advaced level of English language study. The degree of accent was rated on a five-point scale by 13 native speaker judges. It was hypothesized that non-native speakers of English would exhibit greater degree of foreign accent when reading aloud than when recalling a traumatic personal experience. A previous study by Oyama (1982) has found that, contarary to predictions based on native speakers' behaviour in the same task (Labov, 1966), foreign learners of English displayed greater accents during the oral reading task than when telling about a brush with death or about another traumatic time in their lives. It was, therefore, hypothesized that the subjects in this study would perform like the subjects in Oyama's study. The other hypotheses were: 1) the earlier the age at which subjects arrived in Canada, or other English-speaking country, and began learning English, the better their accents would be judged; 2) the greater the number of years spent in Canada, or other English-speaking country, the better their accents would be judged; 3) the native languages of ESL speakers would influence the decisions about the degree of foreign accent made by judges. Taped samples from 29 ESL learners were collected, edited for length, and played to 13 native speaking judges who rated the degree of accent for each speaker heard on a five-point scale. Included on the tape which the judges heard were samples from native speakers to determine intrajudge validity (i.e., how effectively the pronunciation measure differentiated native from non-ntive speakers). Those judges who were unable to identify the speech of native speakers were dropped from the study. Previous researchers have used the mode of the judges' decisions as the appropriate indicator of each subject's accent; in this study, computations were made using both the mode and the mean. They were found to yield nearly identical results in the analyses. Data were analyzed using a multivariate analysis of variance (MANOVA, SPSS X) with the two linguistic contexts as the dependent variables and age of arrival, years in Canada and native language as independent variables. The results showed no difference between the two linguistic contexts, and that age of arrival and native language contributed significantly to the degree of foreign accent while years in Canada did not. Specifically, learners who arrived at a younger age had better accents than those who arrived at an older age. Because subjects were unequally distributed across the languages, it was not possible to determine which native languages are statistically significant in predicting the degree of foreign accent of these learners of English.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,972
Score d'incertitude au seuil0,993

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0080,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,004
Tête enseignante GPT0,203
Écart entre enseignants0,199 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeAutre devis
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2010
Routes d'admission1
Résumé présentoui

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