Dialect Identification Across a Nation-State Border: Perception of Dialectal Variants in Seattle, WA and Vancouver, BC
Notice bibliographique
Résumé
The Atlas of North America English distinguishes "the West" from "Western Canada" on the basis of /æ/ retraction and Canadian Raising (Labov, Ash, and Boberg 2006). Since the Atlas, scholars have provided a more detailed understanding of /æɡ/ raising, /æ/retraction, and Canadian Raising throughout the Western United States and Western Canada (Boberg 2008, Fridland et al. 2016, Presnyakova, Umbal, and Pappas 2017, Roeder, Onosson, and D'Arcy 2018). In a production study, Swan (2016) found that Seattle and Vancouver, BC are differentiated primarily by Canadian Raising and pre-nasal raising of /æ/ and show minimal difference with respect to /æɡ/ raising and /æ/ retraction. Seattle and Vancouver speakers also shared different ideologies about their speech: Seattle respondents felt more confident that they could identify a Vancouver talker based on speech than vice versa. The current study builds from these observations to ask how natives of Seattle and Vancouver perceive the similarities and distinctions documented in the production literature. Can listeners differentiate a talker as being from Seattle or Vancouver? What cues are listeners relying on to judge a talker as being from Seattle or Vancouver? Do these perceptual cues align with the production differences between the cities? What does this imply for a dialectology of the West? These questions are addressed using a forced-choice dialect identification task using the variables represented by FAN, PATH, TAG, and DEVOUT. Our analysis considers signal detection theoretic measures to elucidate sensitivity and bias (Macmillan and Creelman 2005). The results suggest that differentiating Seattle and Vancouver talkers is a challenging task for listeners native to these cities. Neither Seattle nor Vancouver listeners show very accurate performance for any of the single-word stimuli or short phrase blocks of the task and are generally not able classify a talker's city of origin based on their speech. The most accurate performance emerges for Seattle listeners classifying talkers saying DEVOUT, which aligns with the production differences between the cities and is likely driven by stereotypes about Canadian English. Listeners from both cities show more own city bias for the phonetic features that are shown to be more similar across the cities (PATH and TAG) than for those shown to be more different in production (FAN and DEVOUT). A closer look at bias reveals that while Seattle listeners perform with slightly more accuracy, they also show more own-city bias. We discussion possible reasons for this pattern and implications for dialectology of the West and Western Canada.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».