Refashioning the Relationship with the Social World: A Nexus Analysis of a Japanese Queer Immigrant’s English Learning in Canada
Bibliographic record
Abstract
Drawing on ethnographic data, this article chronicles a Japanese queer immigrant’s English learning experiences in a metropolitan area in western Canada. Informed by sociocultural theories and identity-centred approaches to Second Language Acquisition (SLA) research, I highlight the significance and interconnection between social identity, ideology, and language learner agency in second language (L2) learning, teaching, and education. Over a seven-month span, I investigated two research questions targeting the ideological constraints and potential coping strategies in the key participant’s English learning. Nexus analysis is applied as the methodological framework. Following a three-step procedure, I first illustrate my positionality as a researcher and the rationale of the project. I then conduct critical discourse analysis delving into two featured social actions selected from the database generated through ethnographic observations, semi-structured interviews, and participant’s reflective journals. I conclude with a discussion of how SLA researchers and educational practitioners can better support marginalized immigrants’ L2 learning. Building on this case, I propose an allyship-centred approach in L2 teaching and researching and a model of collaborative working between researchers and educational practitioners to provoke more equitable nexuses of practice in immigrants’ L2 education.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".