Confronting Disinformation: Journalists and the Conflict over Truth in #Elxn43
Bibliographic record
Abstract
(Note: This is a pre-print, not copy-edited, of a chapter for publication in: Cyber-Threats to Canadian Democracy, ed. by Holly Ann Garnett and Michael Pal. McGill-Queen’s University Press.) In the run-up to the 2019 federal election in Canada, experts and policymakers raised the possibility that foreign or domestic actors might use disinformation tactics during the campaign. This prompted Canadian journalists to give unprecedented attention to threats that online disinformation might pose to the information ecosystem and thus to electoral integrity. This chapter analyzes how Canadian journalists understood and responded to disinformation in the 2019 federal election campaign.Drawing on interviews with over 30 journalists, we find that while they held competing conceptions of disinformation, most associated it with digitally enabled techniques of media manipulation (e.g. the use of automated social media accounts known as “bots”) pursued by both traditional and newly prominent actors (including foreign states, partisan organizations and loose networks of domestic trolls). To address online disinformation, some journalism organizations developed new reporting approaches and teams, while many journalists and senior editors reflected on how longstanding reporting practices may or may not address this new challenge. We then investigate key challenges that journalists face in countering disinformation by examining three illustrative cases from the 2019 campaign: the alleged role of bots and foreign accounts in online discourse; the salacious rumours about incumbent prime minister Justin Trudeau pushed by foreign and domestic actors, including the U.S.-based website The Buffalo Chronicle; and the potential for leaks of illegally acquired material acquired through hacking operations.Reflecting on disinformation in #elxn43, journalists described three general challenges. Two are relatively new: how to identify novel and sophisticated online disinformation tactics, and how to address disinformation without amplifying its spread on social media. The third is a dilemma that journalists have long faced in election reporting: how to report on misleading claims in a context of intense partisan competition, when journalists themselves are being scrutinized as actors in the political fray.
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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 | 0.001 | 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 | 0.000 |
| 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".