Scholar and Student Wellness while Confronting Violence and Ignorance: Can we Trust our Institutions when we are Targeted?
Bibliographic record
Abstract
As critical scholars of the Far-Right in Canada our work exposes us to acts of violence (both direct and indirect) every day. We are all deeply affected. From different fields (Leisure Studies, Sociology, Anthropology) and different institutions, we have had remarkably similar experiences. As students, we received little or no support to offset the personal impacts of our research programs and had to seek out (or create) our own support networks. As untenured, precarious, and student members of academic research communities, we question whether institutions will stand behind us when we are (inevitably) threatened, or whether we too will need to become victims of violence on campus before we see supportive change. This paper’s narratives highlight voids of support, and it proposes possibilities for change to sustain critical social justice research.
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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 | 0.027 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.085 |
| Scholarly communication | 0.027 | 0.015 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".