Assessing the Values, Definitions, and Structures of Legislative Approaches to Disinformation
Bibliographic record
Abstract
Disinformation is a pressing concern for governments across the world, and Canada is no exception. The nature of Disinformation as information that is intentionally false and purposefully disseminated by habitually anonymous actors for economic, political, or societal gain produces unique difficulties for attempts to control it. The governments of Singapore, Germany, and the United Kingdom have recently passed legislation, and the governments of Brazil and Australia have both drafted bills addressing disinformation. This capstone assesses the values and structures of these laws to identify challenges and opportunities applicable to the Canadian societal and legal context. It also examines the importance of freedom of expression in shaping, implementing, and enforcing these laws. This paper shows that jurisdictions with strong freedom of expression rights prefer legislative approaches that create narrow definitions of disinformation and regulate the behaviour of social media platforms, whereas jurisdictions with weaker freedom of expression rights can more easily create broad definitions of disinformation and directly regulate individual behaviour.
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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 | 0.035 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".