Digital integration in political advertising: Insights from expenditures in the 2019 and 2021 Canadian elections
Bibliographic record
Abstract
Abstract Digital political advertising is “interactive content placed for a fee” (Fowler et al., 2020, 111). It has been long assumed that Canadian political parties engage in digital advertising during and in between campaigns, but how much was being spent and how that spending compared to other types of advertising was impressionistic at best. This all changed in the 2019 federal election when parties were asked to report their spending on digital advertising for the first time. This study examines the extent to which Canada’s political parties have integrated digital political advertising into their overall advertising strategies. To do this, we develop a classification of digital advertising strategies based on relative investment in online and television advertising. The data for this analysis comes from the new expenditure reports of six political parties following the 2019 and 2021 elections. The analysis shows a strategic shift toward digital advertising. In total, Canada’s parties spent more than 10 million dollars on digital political advertising in 2019, increasing to more than $25 million 2 years later, accounting for 25% and 49% of party advertising budgets, respectively. However, the data reveal that parties have very different relationships with digital technologies, shaped by distinct strategic preferences and capacities. It is the first study to systematically analyze this new data source, and it provides a baseline of the state of digital advertising and its place in federal election campaigns.
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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 | 0.006 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".