Exploring Salient Socio-Linguistic Features of African-American English Vernacular
Notice bibliographique
Résumé
This study aimed to highlight the distinguishing socio-linguistic features of African-American English Vernacular. It focused on this variety in terms of different theories concerning its origin and provided a relatively detailed description of the structure of this dialect. Furthermore, the study attempted to discuss the most common educational controversies surrounding the use of this particular variety of English as it is used in America today. Related literature was first reviewed especially that which is directly related to various theories concerning the origin of African-American English Vernacular and how this dialect has developed as a major dialect of American English over time in the United States. Empirically, the data collection process involved eliciting spoken data in naturally occurring circumstances from African-American informants residing in the state of Texas in 2014. The study included twenty male and female informants enrolled at Texas A & M University, College Station in both graduate and undergraduate programs. The researchers used both personal and telephone interviews in the data collection process after obtaining the personal written consent of the informants in both cases. The data were then carefully reviewed and analyzed in an attempt to determine the most salient grammatical, phonological, lexical, and social features of AAEV as it is used in America today. Although a strong correlation between AAEV and Standard American English (SAE) exists, AAEV’s unique origins remain unknown. AAEV is similar to Creole language forms used by many the world over. Phonology traits that differentiate AAEV from other language forms include: Word-final devoicing, reduction of certain diphthong forms to monophthongs . AAEV’s vocabulary is similar to that of Southern informal American dialects. In fact, AAEV users are typically bi-dialectal, meaning that they code-switch between AAEV and SAE often. Debates over AAEV’s use have formed controversial socio-cultural settings with regard to education, particularly that of African-American youth. For example, the Resolution of Oakland held that AAEV had little to do with SAE or any other European language but rather originated from West-African languages. Despite all the controversies surrounding AAEV in terms of its origin or educational role in America today, it might be safe to propose that this particular dialect of American English-call it what you wish-will only receive more attention in modern linguistics and acquire gradual socio-linguistic prestige in the 21 st century. This assumption is primarily based on the overwhelming political changes in the United States today.
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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 | 0,005 | 0,500 |
| 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 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».