Mobilising dilemmatic subject positions: a discourse analysis of an Asian Canadian assistant language teacher’s narrative
Bibliographic record
Abstract
This paper reports on the narrative of Shannon, a Taiwanese Canadian female assistant language teacher (ALT) of the Japan Exchange and Teaching (JET) Programme, one of the world’s largest government-sponsored programmes for recruiting English language teachers to teach overseas [Nagatomo (2016 Nagatomo, D. H. (2016). Identity, gender and teaching English in Japan. Multilingual Matters.[Crossref] , [Google Scholar]). Identity, gender and teaching English in Japan. Multilingual Matters]. The interview data were analyzed with the methodology of critical discursive psychology (CDP), a version of discourse analysis developed by Wetherall and Potter (1992. Mapping the language of racism: Discourse and the legitimation of exploitation. Harvester Wheatsheaf). CDP allows the researchers to engage in ‘double movement’ of Marxist ideological critique and Foucauldian genealogical analysis, with which I demonstrate how Shannon’s subject position was conditioned by broader ideologies and discourses about race, gender, and language. My analysis highlights how the ideology of native speakerism impacts an Asian native speaker’s experience as an ALT in Japan with a particular focus on Shannon’s struggle in a dilemmatic subject position between Asian Self and foreign Other. Due to her Asian appearance, Shannon constantly felt that she was ‘not foreign enough’ to fulfil the role of an exoticised native speaker and struggled to negotiate her legitimacy as an English teacher. Taking dilemma as a generative space for change, I discuss potential pathways for ideological critique and discursive transformation.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.051 | 0.031 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".