Towards Sustainable Practices of Diversity and Inclusion of SOGIESC in Japanese Language Education & Japanese Studies
Bibliographic record
Abstract
Research into Japanese language education and the intersections of gender and sexuality has demonstrated the importance of critiquing heteronormative biases in teaching materials and resources. We propose that collaborative approaches which decentre regimental heteronormative understandings of the Japanese language and which facilitate inclusivity and affirmation of sexual orientation, gender identity, gender expression, and sex characteristics (SOGIESC) are crucial to sustainable practices in Japanese language education at all levels. Adopting a critical reflexive stance, we first trace the community advocacy which has resulted in changes to representations of sexual identities in Japanese dictionaries. We next critically examine Japanese language education materials which are used across a diversity of locales before offering some practical ways in which small changes can be made to ensure greater inclusion and affirmation of SOGIESC in local learning environments. Further, we discuss the importance of developing metalinguistic awareness in relation to gendered speech styles and language ideologies. We argue that collaboration and co-construction are fundamental to sustainable practices which learn from histories of advocacy and research, are responsive to shifts in Japanese society and culture, and adaptable to a diversity of learning environments.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.012 |
| 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".