“Non-nativeness” and Its Critical Implications on Non-Native English Speaking Teachers in an L1 Context
Bibliographic record
Abstract
This research paper focuses on critical implications of the concept of 'non-nativeness' on NNESTs (Non-native English Speaking Teachers) in terms of hiring and employability, their identity as teachers, and perceptions surrounding their proficiency.This small-scale study presents findings and critical analyses on the basis of interviews conducted with a group of NNESTs in an L1 (English is spoken as the first language) situation and with a critical agenda to explore their perceptions about their employability, their identity as teachers, and their proficiency.The study suggests that NNESTs undergo a period of realization through fear of being a non-native, expectations of being accepted, conformation to commonly accepted standards, and measuring success by means of standardized modus operandi of performance evaluation and teaching methodology.The research concludes with presenting some tools and instruments to empower NNESTs teaching in an L1 situation in general and in the context under study, in particular.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.041 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".