Online Disinformation and Harmful Speech: Dangers for Democratic Participation and Possible Policy Responses
Bibliographic record
Abstract
In advance of the 2019 federal election, the Canadian government began to address challenges posed by posed by new digital technologies and the rapidly-evolving information system. These policy responses include revisions to Canada’s election law and the creation of a federal task force to monitor and respond to online interference during an election campaign. However, much can still be done to uphold the communications element of electoral integrity, both by government and by other stakeholders including journalists, social media companies, and civil society organizations. This article focusses on two related challenges: disinformation and harmful speech online. By ‘‘disinformation,” we refer to intentionally false or deceptive communication to advance political ends. We use the term ‘‘harmful speech” to refer to communication that is abusive, threatening, denigrating, or that incites violence. We clarify the risks that disinformation and harmful speech pose to democratic engagement and democratic processes, and synthesize current research about their creation, circulation, and political impacts. We then examine the current regulatory context in Canada (at the time of writing, February 1, 2019) and highlight gaps. We conclude with policy recommendations that would enable the Canadian government, social media platforms and journalism organizations to better understand and reduce the threats to democracy posed by disinformation and harmful speech. These are partly drawn from policies that other countries are pursuing. We call for a three-pronged policy framework: 1) greater enforcement of existing laws, 2) regulation to encourage and help social media platforms address abuses; and 3) improved civil society measures, especially by journalism organizations. Previously published in the 2019 Special Issue of the Journal of Parliamentary and Political Law entitled The Informed Citizens’ Guide to Elections: Current Developments in Democracy (Toronto: Thomson Reuters Canada, 2019).
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".