Fringe Politics: The Deep Web’s Impact on the 2019 Canadian Election
Bibliographic record
Abstract
We investigated how political memes, language, and shared political objects (videos, photos, images, graphics, posts, etc.) from fringe websites became insinuated into mainstream political conversation on more established social media platforms and news properties in discussions of the 2019 Canadian federal election. In contrast to the popular theory of “fake news” as the product of foreign interference, our hypothesis was that much of the democratically disruptive content making its way to social media and news platforms originates on non-mainstream internet spaces such as 4chan/pol/ and Reddit. There is a distinct lack of critical scientific study in Canada about how extremist content makes its way on to mainstream platforms during election cycles; how this content is picked up by commentators on mainstream platforms; and the effect that this has on contemporary political debates and elections. This study provides insight on how marginal political actors’ dark web content intervened, or was actively co-opted by other political interests and groups, to influence the fall 2019 election. The findings will contribute to ongoing discussions about the governance and regulation of elections, political parties and candidates in the context of online media properties and 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".