Creative Maladjustment and Creative Subversion of Kazakhstan’s English Teachers: Doing Translanguaging in Concealment
Bibliographic record
Abstract
Kazakhstan’s government has been pushing for English proficiency to become globally competitive. Rhetoric about global competitiveness has resulted in various policies to promote English teaching and learning in primary, secondary, and tertiary institutions. English has eventually evolved as the language of instruction in many universities. The direct language teaching approach has been the mainstay for teaching English as a foreign language (EFL), whereby school administrators do not permit teachers to use the mother tongue. Instead, they are to teach English monolingually. With the monolingual approach to teaching, the teacher does not need to know the local languages of the students and is supposed to be a native speaker or have native-like proficiency in the English language. Most of the English teachers in Kazakhstan are local. This qualitative study explored English teachers’ experiences in elementary and secondary schools in Kazakhstan and how they navigated their daily lives in the classroom teaching EFL. The findings indicated that teachers have long been practicing creative maladjustment and creative subversion by utilizing a translanguaging approach to teaching English without knowing the efficacy of translanguaging in language learning. This article suggests introducing and developing translingual art-based practices for teaching and learning English in Kazakhstan.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".