Deconstructing Non-Native English-Speaking Teachers’ Professional Identity: Looking Through a Hybrid Lens
Bibliographic record
Abstract
This article critically analyses how the construct of non-native English-speaking teachers’ (NNESTs) professional identity has evolved in the context of binary logic and power relations. From a socio-historical point of view, colonial origin of English language teaching and native speaker ideology have been identified as prominent discursive influences of NNESTs’ professional identity. Although influenced by (post)colonial discourses, research into NNESTs’ professional identity is now veering off to a new direction that questions the binary logic and explores experiences beyond the boundary between the native self and the non-native other. This paper argues that nativeness and non-nativeness are not mutually exclusive and objective categories for NNESTs’ professional identity. Their identity is rather subject to constant innovation and plasticity. Beyond the critical analysis of the native speaker construct, the paper proposes a professional conceptualisation of a hybrid professional identity of NNESTs in a third space of reflection, enunciation and productive articulation.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| 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".