Plurilingualism for Transformative Social Justice in Language Education: A New Perspective
Bibliographic record
Abstract
The question of social justice in language education (LE) became prominent due to increased linguistic and cultural diversity fuelled by globalization and migration, which exacerbated social inequality in this neoliberal era. The critical “turn” in education resulted in the emphasis on issues of social inequality, racial discrimination, and decolonization of curriculum, among others. Nancy Fraser’s tripartite theory (3Rs), where social, cultural, and political injustices are compensated for by redistribution, recognition, and representation, has the potential to address social justice issues in LE. This theory has already been applied to education research, but not extensively. The purpose of this paper is to explore plurilingualism for social justice in LE by analysing its possibilities for cultural recognition, economic redistribution, and political representation and present a new understanding of the concept of social justice in LE through recognition, redistribution, and representation.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.074 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".