To Regulate or Not to Regulate? The Future of Disinformation in Canada
Bibliographic record
Abstract
The rise in technology in recent years has ushered in massive contributions to social discourse and democratic expression by increasing accessibility through the Internet. However, at the same time, the advancement in online engagement has been met with a rise in disinformation. Disinformation is defined as false or misleading information that is deliberately created and disseminated with the intent to deceive. This rapid spread of disinformation is troublesome as it has the ability to generate mass public disapproval, thereby accelerating instances of violence in society as well as increased political polarization. Moreover, increased disinformation harms social institutions such as the health care system which leads to decreased health outcomes. All in all, disinformation is an evolving threat that requires a comprehensive solution to address it. Currently, regulation in Canada is outdated and holds social media platforms to minimal accountability. Electoral laws such as the Canada Elections Act and the Elections Modernization Act have been highly inadequate to deal with digitalization. Many academics and media specialists advocate for substantial changes. The most direct solution to date dealing with regulating the internet has been in the form of Bill C-10 which seeks to update the Broadcasting Act. This bill, as well as other government initiatives still do not effectively address disinformation directly meaning that electoral processes will still be negatively affected. Moving forward, the government needs to establish policies that directly tackle the factors that allow for the proliferation of disinformation on digital platforms. This includes greater transparency from big tech giants, the enactment of takedown laws of harmful content online as well as increased media literacy initiatives. Taken together, these steps could help in drastically reducing the impact of disinformation on online platforms.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.031 | 0.020 |
| Scholarly communication | 0.024 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 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".