Narratively performed role identities of visible ethnic minority, native English speaking teachers in TESOL
Bibliographic record
Abstract
The binary distinction of ?native speaker? and ?non-native speaker? (NS/NNS) remains the primary way in which professionals are categorised in the field of teaching English to speakers of other languages (TESOL). This distinction is problematic because it is used to place greater value on native English speaking teachers (NEST) over non-native English speaking teachers (NNESTs). This distinction is argued to be largely based on linguistic features (Medgyes 1992; Cook 1999). However the aspect of race remains to be adequately discussed (Kubota and Lin 2006).This thesis has its origins in my personal experiences with racism because, as a Canadian- Filipino, my employer and my students did not accept me as a ?real? NEST because I am ?non-white?. In my initial research, during my MA TESOL, into the professional experiences of racism I coined the acronym ?VEM-NEST?: visible ethnic minority, native English speaking teacher. I used this term to describe the particular group of teachers, to which I belong, who do not easily fit into the available categories of NS/NNS, and consequently NEST/NNEST.My thesis reported on the experiences of nine VEM-NESTs and how they performed specific identities during specific events. Their experiences were presented as individual restoried narratives which were developed from the combination of the participants? written stories and one-to-one interviews. The restoried narratives were analysed using an analytical lens based on Labov and Waletzky?s (1967) structural approach.The findings suggest that VEM-NESTs need to meet a certain amount of ?native speaker? norms in order to be given the opportunity to perform their VEM-NEST role identities in specific situations. This has particular implications for how the NS/NNS binary distinction needs a more nuanced understanding as a way of addressing the inequalities embedded in the way TESOL professionals are valued.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".