“Sexualized Online Bullying” Through an Equality Lens: Missed Opportunity in AB v. Bragg?
Bibliographic record
Abstract
In AB v. Bragg, the Supreme Court of Canada ruled that fifteen-year-old AB should be allowed to use a pseudonym in seeking an order to disclose the identity of her online attacker. By framing the case as one pitting the privacy interests of a youthful victim of sexualized online bullying against principles protecting the free press and open courts, the SCC approached but ultimately skirted the central issue of equality. Without undermining the important precedent that AB achieved for youthful targets of online sexualized bullying, the author explores the case as a missed opportunity to examine the discriminatory tropes and structural inequalities that undergird the power of this kind of bullying. Viewed through an equality lens, enhanced access to pseudonymity for targets is not necessarily about privacy per se, but rather an interim measure to respond to the equality-undermining effects of sexualized online bullying—a privacy mechanism in service of equality.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.024 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.019 |
| 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".