Speaking Freely vs. Dignitary Harm: Balancing Students’ Freedom of Expression and Associational Rights with their Right to an Equitable Learning Environment
Bibliographic record
Abstract
In this article, I examine the difficulty of using student codes of conduct and civility policies as a way to restrict harmful speech. I argue that policies used to monitor students’ non-academic behaviour provide administrators with a means to restrict and surveil students’ political advocacy work, especially marginalized students’ advocacy. Rather than providing a ‘safe’ learning environment, codes of conduct curtail students’ opportunities for freedom of expression and limits their ability for critical pedagogical engagement with controversial ideas. Drawing on case studies at Canadian universities, I illustrate the contradictory challenges that student activists encounter when attempting to balance principles of freedom of expression and principles of equity on university campuses. Rather than use codes of conduct, I argue that administrators should adopt criteria that help students identify and limit dignitary harms. In doing so, students will be better equipped to assess their expressive freedom and associational rights with the rights of others to an equitable learning environment. Moreover, such an approach represents a decolonial shift and promises to expand our narrow liberal conception of rights and ensure marginalized peoples’ voices and worldviews are heard.
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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 | 0.037 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.121 |
| Scholarly communication | 0.027 | 0.014 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.006 | 0.011 |
| 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".