Freedom of Speech on Social Media: How Canada’s Online Harms Act Goes Too Far
Bibliographic record
Abstract
This essay will examine how Canada’s Online Harms Act affects freedom of speech, specifically on social media platforms. While the bill has good intentions, such as stopping child exploitation, its broad scope carries the risks of restricting free speech. The Online Harms Act (also known as Bill C-63) will be analyzed through John Stuart Mill’s Harm Principle and Joel Feinberg’s Offense Principle. The paper argues that while restrictions on online speech are justified in extreme cases to prevent harm, Bill C-63 goes too far— imposing harsh penalties such as fines or imprisonment. Furthermore, these penalties can even be applied to cases where a person might engage in future speech deemed harmful. The essay contends that the Online Harms Act should be narrowed in scope, focusing on the most serious violations. A balance must be struck between having online safety and upholding the right to freedom of speech.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.045 | 0.030 |
| Scholarly communication | 0.022 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 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".