Dialogues of Disruption: Confronting Oppression in the Academy
Bibliographic record
Abstract
Within academia in recent years, there has been a concerted effort to re-center the perspectives and rights to free speech of the status quo at the expense of the safety and wellbeing of queer, trans, racialized, and Indigenous communities. Historically, critical race scholars have identified the centering of freedom of speech as an exercise by the old-guard in white supremacist culture to repurpose and repackage language about political freedoms in an effort to retain white settler control of a society that has long outgrown stunted ideologies about binary gender norms, and the continued oppression of Black, Indigenous, and racialized communities. Through our hard-fought lessons learned from often painful lived experiences as queer, trans, and Black, Indigenous, People of Colour (BIPOC) scholars and activists, this paper aims to: 1) Archive and document the testimonies and experiences of multiply-marginalized students and emergent faculty in the field of community psychology in a mid-sized Canadian university; 2) Utilize critical and intersectional analyses in unpacking the layers of violence and harm expressed and experienced through case examples; 3) Use our experiences to share strategies on the successful navigation of white supremacy in the academic spaces in which we work and learn; and 4) Call academic disciplines, including community psychology, to action by identifying their ethical responsibility to cultivate non-violent spaces for BIPOC people.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.107 | 0.132 |
| Scholarly communication | 0.033 | 0.012 |
| Open science | 0.005 | 0.034 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".