Facebook Campaigning in the 2019 and 2021 Canadian Federal Elections
Bibliographic record
Abstract
Canada’s federal elections in 2019 and 2021 produced a similar outcome—a minority Liberal government. These back-to-back elections provide an ideal context to understand trends in digital campaigning strategies and assess how the pandemic influenced campaigns’ use of social media. We examine how the three leaders of the major parties used Facebook in 2019 (n = 712) compared to 2021 (n = 979). The Conservative leader O’Toole posted more frequently than other candidates in 2021, fitting with the equalization theory of digital campaigning. In 2019 and 2021, the incumbent prime minister, Trudeau, received the most user engagement on his Facebook posts despite calling a snap election during a pandemic and less than two years into his mandate. These findings support normalization theories of digital campaigning with evidence of an accumulating incumbent advantage. The Covid-19 pandemic sidelined attention to climate change. We argue that the Liberal government owned both issues; we expected Trudeau to have greater attention to and user engagement for these policy posts. In general, Facebook posts about the pandemic yielded greater user engagement than posts that did not mention the pandemic. Candidates tested new campaign strategies in 2021, particularly making calls to interact with them; these posts yielded higher user engagement than posts that did not include a call to interact. While candidates used new social media campaign strategies, voter turnout declined from 2019 to 2021. These findings have implications for other democratic systems and the future of digital campaigning.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".