Illuminating language users in the discourse of linguistic diversity: toward justice-informed language education
Bibliographic record
Abstract
Abstract The field of language education has mobilized diversity paradigms during the last several decades. Paradigms, such as world Englishes, English as a lingua franca, and translanguaging, have illuminated how linguistic forms and practices vary across locations, contexts, and individual linguistic repertoires. Although they aim to raise teachers’ and students’ engagement with linguistic heterogeneity, they are largely founded on the postmodern/poststructuralist valorization of linguistic hybridity and fluidity, which tends to neglect language users and thus overlooks the human differences that also inform that heterogeneity. True linguistic diversity and justice can be attained by both problematizing structural obstacles and recognizing that ideologies and structures are entrenched in unequal and unjust relations of power regarding race, gender, class, and sexuality, which influence diverse language users to communicate in certain ways. This conceptual paper problematizes the conventional focus on language in the discourse of linguistic diversity within language education, especially English language teaching, and proposes that we pay greater attention to language users. While recognizing that social justice is not a universal notion, we endorse an antiracist justice-informed contextualized approach to teaching about linguistic diversity by illuminating how diversity and power among language users as well as broader structures impact the nature of communication.
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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 | 0.001 | 0.021 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".