Interpersonal Factors Affecting Queer Second or Foreign Language Learners’ Identity Management in Class
Bibliographic record
Abstract
Abstract Although most second or foreign language classrooms can be assumed to contain queer learners, research shows that educators often fail to create inclusive learning environments and outcomes for these populations. A step toward improving this situation is to understand the factors affecting how queer students manage their identities in language classrooms. I use interview data from queer students of Japanese as a second or foreign language ( N = 16) to analyze how the characteristics and behaviors of others in the class influence their sexual identity management. My findings indicate that queer learners use 3 types of cue when gauging others to anticipate the consequences of their decision to reveal their nonheteronormative identity in class: salient indicators , insider evidence , and explicit statements . This article also explores how some participants used the “here and now” event of the research interview itself to carry out accounting work that discursively resisted heteronormativity by positioning their sexual identity as normative. I conclude by discussing the implications of these findings for identity management theory and educational practice.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".