The ‘Half-Visible’ Teacher: Experiences of a Hong Kong Canadian English Teacher in Japan
Bibliographic record
Abstract
In English education in Japan, there has been a historical tendency to view foreign teachers of a specific race and/or ethnicity favorably as representatives of the target language and culture, regardless of their individual ability to teach. These discriminatory biases have unfortunately hindered other groups of teachers from fully embracing their identity, sometimes resulting in self-perceptions synonymous with the phenomenon of impostor syndrome. In this chapter, the author adopts an autoethnographic approach to illustrate his professional experiences as a Hong Kong Canadian through different stages of his English teaching career in Japan which include team teaching, English conversation schools, and university teaching. It demonstrates how the clash between his identity as a non-Japanese Asian teacher and the idealized image of the native English-speaking teacher in Japan had haunted him during each stage of his career. The latter sections of the chapter focus on the conclusions reached by the author after almost a decade of struggling with these identity complexes, as well as suggestions for changes and actions to positively move forward from these complexes and contribute meaningfully to society.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.039 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".