English Language Learners, Labels, Purposes, Standard English, Whiteness, Deficit Views, and Unproblematic Framings: Toward Southern Decoloniality
Bibliographic record
Abstract
This paper argues for revisiting ways in which English Language Learners (ELLs), and the learner labels attributed to them, are negatively, racially, and pathologically framed and constructed based on, putatively, their English language competence, or their lack of it. It contends that this framing tends to give rise to a raciolinguistic profiling of these learners, as they end up being classified by their race, pan-ethnicity, nationality, immigrant/refugee status, regionality, and at times, by their skin color, in addition to their language abilities. This raciolinguistic framing often engenders other framings such as White, deficit, and poverty framings, and sub-framings like an othering framing (e.g., the racial others and the linguistic others). These framings, together with the normative ways in which ELLs’ language problems are constructed, have been characterized in this paper as misframings. Additionally, employing southern decoloniality, the paper problematizes and critiques the way ELLs are constructed and labeled, and the appropriation of Standard English (SE) as the sole touchstone of acceptable English in the midst of the other varieties of SE and of pluriversal speakers of English. Finally, the paper calls for the provincialization/localization or the deparochialization of English in keeping with its southern decolonial approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".