The master's tools will never dismantle the master's school: Interrogating settler colonial logics in language education
Bibliographic record
Abstract
Abstract Racialized students are overrepresented in special- and English-learner education programs in the United States. Researchers have pointed to implicit bias in evaluation tools and evaluators as a cause resulting in calls for more culturally competent/relevant practices/assessments. However, this paper argues that racial overrepresentation is reflective of larger settler colonial frameworks embedded in linguistic standards that continue to drive education and language ideologies/practices globally but especially in U.S. schools. First, through an analysis of an orthoepic test used during the Parsley Massacre of 1937 on the island of Hispaniola, I present how the evaluation of accented language has been used to racialize and pathologize people. Secondly, through a comparative analysis of bilingualism in the U.S. and Canada, I show how linguistic variation is only devalued when it emerges from marginalized communities, affirming the white normative gaze as a mechanism for maintaining inequitable power structures. Finally, the paper presents how these logics are present in current manifestations of bilingual education. By indicating how racially, physically, and/or neurodivergent people are othered, this paper calls on the decolonization of applied linguistics in order to effectively address the over- and disproportionate representation of Black, Indigenous, and/or Latinx students within special- and English-learner programs.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.043 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".