Social Media and Democracy: Challenges for Election Law and Administration in Canada
Bibliographic record
Abstract
This article considers the challenges posed by social media for election law and administration in the Canadian context, particularly in relation to political advertising. I argue that the widespread use of social media by voters, political parties, and interest groups requires policy responses in order to promote the effectiveness of election administration and to further its underlying values. I use the term “social media” primarily in relation to Facebook and Twitter. Social media is already an important component of politics in Canada, including for political advertising. Three of the basic tools evident in Canadian election law include: (1) transparency/disclosure rules, (2) restrictions on spending, and (3) enforcement measures. The emergence of social media has challenged the capacity of each of these pillars of the legal regime to further their intended goals or the underlying values that motivated their introduction. The existing legal rules that regulate electoral activity off-line appear to be largely inadequate for a world in which social media is an important part of political communication and advertising. Either they do not apply to online politics or, if they do, the law has been under-enforced or exposed as out of date. Compounding these problems has been a series of decisions by Elections Canada to narrowly interpret its authority to regulate online politics, so as to leave large gaps in regulatory oversight of social media. In response, I propose several changes to the Canadian legal regime. They aim to promote effective election administration consistent with the values that inform the legislation. These proposals include enhanced disclosure rules for political advertising on social media, a separate social media spending limit for political parties and interest groups, and enhanced regulation of social media platforms, including by treating them as “broadcasters” for specific purposes under the Elections Act.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.046 | 0.022 |
| Scholarly communication | 0.028 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| 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".