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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".