Battling for Votes: The Ascent of the Permanent Campaign in Canada
Bibliographic record
Abstract
This paper describes a recent shift being seen in Canadian politics. By studying the concept of the permanent campaign, it can be seen that voters are involved in politics in a new way. The permanent campaign is characterized by how it increasingly uses recent and new technologies in a sophisticated manner. This includes what is known as Web 2.0, which is seen with the broader widespread usage of the internet as well as social media platforms. Web 2.0 makes practices of data collecting possible, such as microtargeting and narrowcasting. The permanent campaign is also evident in the changing landscape of news media. These various techniques of using technology in Canadian politics shapes the way that the electorate receives messages. There are differing opinions as to whether this shift is positive or negative for Canadian politics. Journalists tend to view the permanent campaign as harmful while some authors view things like social media as more participatory.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".