Texts and countertexts: dismantling dominating discourses through decolonizing and antiracist pedagogies
Bibliographic record
Abstract
In the context of resurgent nativism and political backlash against diversity, equity, and inclusion, this special issue of Language Awareness examines how language educators across global contexts are enacting decolonizing and antiracist pedagogies to dismantle dominating discourses that continue to structure classroom life. Drawing on a range of critical theories, contributors explore how colonial and racist ideologies are reproduced through curriculum, assessment, policy, and interaction, and how these logics can be challenged through critical engagement with texts and countertexts. The special issue foregrounds decolonizing and antiracist literacies as critical practices and dispositions that refuse deficit framings of racialised learners, legitimize subaltern knowledges, and cultivate language awareness grounded in social justice. Contributors document how teachers and learners mobilize countertexts to reframe epistemic authority, contest Eurocentric norms, and reimagine classrooms as sites of resistance and solidarity. While acknowledging institutional and ideological constraints, the special issue affirms that language education can serve as a space for transformative pedagogical practice. Together, these articles offer frameworks and empirical illustrations for how language teachers and learners might critically read, write, and reauthor the word and the world.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.068 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| 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".