Bibliographic record
Abstract
Teachers of English to Speakers of Other Languages (TESOL) scholars have recently drawn on critical race theory (CRT) to critique and understand the propagation of Whiteness as a norm associated with native English speakers. However, the area of language studies, more broadly defined, has yet to develop the same link with CRT. To this end, this article proposes and introduces an emerging theoretical and analytical framework called LangCrit, or Critical Language and Race Theory. LangCrit puts the intersection of the subject-as-heard and the subject-as-seen at the forefront of interpretation and analysis. This article urges language studies scholars, both within the field of English language teaching and beyond, to continue to look for ways in which race, racism, and racialization intersect with issues of language, belonging, and identity.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".