Grappling with Cisgender Positionality in Applied Linguistics Research with Trans Participants
Bibliographic record
Abstract
Abstract Cisgender researchers can and must take action against transphobia within our spheres of influence (Knisely, 2023; Zimman, 2021). However, these processes, while necessary, can be fraught and problematic. Trans scholars have challenged the exploitative, cisnormative, and appropriative nature of much academic research by cisgender people on transgender people (Radi, 2019). How can cisgender researchers engage in the necessary work of opposing cissexism in our discipline, and can we do so without perpetuating a cycle of epistemic violence? In this paper, I offer critical reflections as a cisgender researcher on designing a research project on the language learning experiences of non‐binary youth, grounded in scholarship by trans thinkers (Keenan, 2022; Nicolazzo (2017)), and collaborative research methodologies (Jourian & Nicolazzo, 2017; Mayo, 2017). I first discuss my own positionality as a cisgender individual. I then present three insights into what cisgender positionality means and the implications of these for researchers in Applied Linguistics. First, cisgender positionality means having limited personal experience with gender as complex and multidimensional, which requires nuancing how we ask questions related to identity, socialization, language, and embodiment. Second, cisgender positionality means being positioned as epistemic authorities, which necessitates deliberately centering participants as experts and analysts of their experiences. Third, cisgender positionality means benefiting from unearned privilege, which compels us to design research that directly benefits trans individuals and communities. Finally, I propose a critical self‐reflection protocol for cisgender researchers for engaging ethically with trans participants.
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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 | 0.070 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.036 | 0.039 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".