“Противоречат, да, мои слова?!” Translingual TESOL Teacher Educators’ Conflicting Translanguaging Beliefs
Bibliographic record
Abstract
This qualitative study explores translanguaging beliefs among translingual TESOL teacher educators in connection to broader sociolinguistic realities and ideological frameworks in Qazaqstan’s multilingual context. Drawing on data from semi-structured interviews with ten faculty members of two teacher education programs, the study identifies three distinct yet contradictory translanguaging belief categories: hegemonic, resistant, and transformative. While participants often viewed translanguaging as a natural and dynamic element of their linguistic repertoires, they expressed ambivalence about its pedagogical application. Hegemonic beliefs, rooted in monoglossic ideologies, led them to prioritize English-only instruction in line with systematically enforced native-speaker ideals and the monoglossic stance of the Qazaqstani trilingualism policy. Resistant beliefs treated translanguaging as an unavoidable crutch, reflecting negative affect and internalized deficit ideologies and racism. Conversely, transformative beliefs embraced translanguaging as a valuable pedagogical tool and leveraged its potential to scaffold learning, foster inclusivity, and affirm students’ multilingual identities. The study also underscores the need for professional development and policy reforms to promote critical reflexivity for ideological clarity and equip teacher educators with translanguaging strategies aligned with Qazaqstan’s translingual realities. By disrupting oppressive ideologies and shifting toward transformative translanguaging beliefs, translingual TESOL educators can model asset-based pedagogies to pre-service teachers, paving the way for socially just multilingual education grounded in heteroglossic language ideologies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".