Speaker evaluations in multilingual contexts: The predictive role of language and nationality attitudes as distinct factors in explicit and implicit cognition
Notice bibliographique
Résumé
Previous speaker evaluation models have assumed that, in social interactions, attitudes towards languages are the most salient cues that are used to form an evaluation of the person who is speaking (Giles & Marlow, 2011). The role of attitudes towards the speaker’s national group has not been addressed because most studies have been conducted in monolingual contexts in which spoken language serves as a clear indicator of national group membership. However, the concepts of language and nationality cannot be equated in multilingual societies, which are characterized by various nationals using different languages. The present dissertation addresses the need for the development of a revised theoretical model for multilingual contexts by making a distinction between language, nationality, and speaker concepts on both an explicit and an implicit level. The adapted model applies social-cognitive theories that propose a distinction between explicit and implicit processes and further posits a differential predictive influence of explicit and implicit attitudes on explicit and implicit speaker evaluations. In the multilingual context of Luxembourg, three successive studies were conducted by adapting an audio-based Implicit Association Test (IAT) as an implicit measure of language and nationality attitudes and an evaluative priming task as implicit measure of speaker evaluations. The findings emphasized the convergent and discriminant validity of language and nationality attitudes on both an explicit and an implicit level. Furthermore, the distinctness of explicit and implicit speaker evaluations was confirmed such that explicit evaluations were influenced by explicit attitudes, and implicit evaluations were affected by implicit attitudes. In the fourth study, the model was transferred to the linguistic context of Montreal (Canada). The findings showed that implicit speaker preferences were affected by implicit nationality attitudes affirming model transferability. Overall, the dissertation shows that language is a salient factor in explicit person perception, whereas nationality plays a vital role on an implicit level, demonstrating the added value of the language-nationality and the explicit-implicit distinction in the speaker evaluation formation. Self-reports diverged from implicit measures such that an in-group bias was visible only on an implicit level, giving insight into the effect of specific socio-contextual factors in a given linguistic context as well as the practical implications for decision makers in professional domains.
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 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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| 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 ».