Differential Diagnosis in Online Regulation: Reframing Canada’s “Systems-Based” Approach
Bibliographic record
Abstract
In February 2024, following Germany’s “Netzwerkdurchsetzungsgesetz”, the European Union’s Digital Services Act, and the United Kingdom’s Online Safety Act, Canada exploited its “second mover” regulatory status by introducing its long-awaited Bill C-63. Through its Online Harms Act and related amendments, it proposed an innovative “systems-based risk assessment” model for regulating harmful online content. In this article, the authors argue that any truly “systems-based” approach will benefit from regulatory insights and prescriptions informed by the following two interdisciplinary sources. First, both constitutional and media law scholars endorse stepping outside conventional regulatory models by employing more “context-based” or holistic approaches—a regulatory turn seemingly consistent with Canada’s pivot towards an innovative “systems-based” model. Second, exploring further the synergies between law and medicine introduced in our previous Digital Iatrogenesis eucrim article, any enhanced framework aimed at “cracking the code” of digital media regulation will benefit from profound insights native to social medicine and diagnostic theory. Besides providing a convincing case for expanding aetiological (and regulatory) inquiry to include social and environmental factors, established principles of medical diagnosis provide a valuable decision-making protocol for present-day regulators. Taken together, leading regulatory and medico-diagnostic scholarship suggests that prevailing “systems-based” models—as epitomised by Canada’s proposed Online Harms Act—would appear to function as a “blueprint” for privatised government censorship, providing regulators with the legislative mandate, informational transparency, and compliance authority necessary for regulatory capture. As one of the Internet’s “Big Picture” dilemmas, these censorship concerns may yet require reassessment of Europe’s current regulatory framework.
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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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.021 | 0.093 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".