Disinformation, Digital Information Equality, and Electoral Integrity
Bibliographic record
Abstract
Electoral disinformation campaigns intentionally deceive voters, thereby disrupting the notion of fair elections and challenging western democracies to craft new policies that safeguard their electoral integrity. Over-regulation of political speech, however, can imperil the political participation of the informed voter, who in turn engages and influences other voters. Such policies for regulating electoral disinformation must therefore balance the tension between curbing speech and encouraging voters to engage in political participation through the free flow of information. Canadian elections law offers a possible solution for jurisdictions seeking to effectively regulate disinformation without unduly stifling free expression through the principle of informational equality. We argue for an updated principle of digital information equality to address the harms of disinformation. By reinvigorating the principle of information equality and adapting it from a theoretical concept to a regulatory device, this article proposes a new method to regulate electoral disinformation while supporting an informed electorate, respecting democratic principles, and protecting electoral integrity. In so doing, the article identifies three harmful examples of electoral disinformation that warrant increased regulation and concludes with recommendations for other jurisdictions seeking to regulate disinformation in the electoral context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| 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".