Trans Precarity under Conditions of Resurgent Far-right Extremism in Pedagogical and Campus Spaces
Bibliographic record
Abstract
This chapter focuses on our own experiences in the academy to reflect on how the current context of anti-trans backlash manifests in our everyday lives working and studying in a Faculty of Education. We situate our experiences within a Trans Studies informed analysis of the conditions of intensified trans precarity and the demands they place on questions pertaining to safety in the classroom and on campus for trans and queer students. We focus on our own experiences in the classroom and also in conducting research with trans students on campus to illuminate how an emboldened expression and enactment of anti-trans hate outside of the university context is seeping into pedagogical spaces, which speaks to the diffuseness of such anti-trans rhetoric that crosses national borders. Indeed, it is the weaponization of transphobia that is supported by white supremacist groups that has materialized in campus spaces, which grounds the empirical basis of the critical insights that we share in this chapter. We expose the necropolitical conditions underscoring these encounters to shed light on the material manifestation of a resurgent far-right extremism as a networked political system of what Kallis refers to as “banal fascism”—a system with a particular cis White Nationalist fervor and influence for us in Canada emanating from the epicenter of the US where the propagation of conspiracy theories and post-truth proliferate and suffuse our everyday lives in the university as educators and researchers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.031 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".