Exploring Salient Socio-Linguistic Features of African-American English Vernacular
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.500 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".