Bibliographic record
Abstract
Online harassment, cyberbullying, hate, and other forms of online abuse pose a significant threat to human rights in Canada. Now, the country is at a crossroads: it will face American pressure to adopt a broad immunity model similar to Section 230 of the Communications Decency Act or, at long last, take more robust action to address cyberharassment and other online abuse, beyond the piecemeal approach used today. Central to this regulatory debate are concerns and claims about “chilling effects”— that is, the idea that certain regulatory actions may “chill” or deter people from exercising their rights online and in other digital contexts. Such claims have long been raised to oppose measures addressing online abuse, particular speech chill. In this chapter, I argue that such chilling effect claims advanced to oppose measures taken to curb online harassment and abuse neglect other kinds of chilling effects—how such abuse chills the rights of victims. And, drawing on new empirical research on this point, I argue that such legal interventions—like cyberharassment laws—rather than having a chilling effect, can also have a salutary impact on the speech and engagement of victims whose voices have been typically marginalized. I will also discuss the important implications these findings have for Canadian law and policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".