Digital integration in political advertising: Insights from expenditures in the 2019 and 2021 Canadian elections
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it