Second Language Writing and Bidialectalism: A Case for African American Student Writers
Bibliographic record
Abstract
There has been much research conducted on second language writing. In addition, there exists a significant amount of studies conducted with African American student writers. However, the fields of Second Language Writing and Composition Studies rarely if ever dovetail in the research literature. The purpose of this article is to argue how English language learners and bidialectal (English as a second dialect) learners share similar learning experiences and how sociocultural theories of English language pedagogy can inform composition theory, specifically as it relates to African American student writers. The study of writer identity provides insights into both bilingual and bidialectal learners’ authorial identity constructions and their experiences in English language learning contexts. Based on these similarities, I argue the need for composition theory to integrate sociocultural theories of second language learning and identity to better address the needs of bidialectal learners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.035 | 0.017 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".