The Canadian Punitive Paradox: The Evolution of Conservative Political Marketing Practices and the Late Onset of Penal Populism in Canadian Federal Politics
Bibliographic record
Abstract
At the height of the punitive turn that occurred among Western liberal democracies, Canadian federal politics stands out as exceptional for choosing not to embrace a ‘tough-on-crime’ agenda. Instead, penal populism emerged as a salient political marketing tool in Canadian federal politics much later — at a time when many of the failures associated with its outcomes were coming to light elsewhere. The latent emergence of penal populism in Canadian federal politics was atypical as it often surfaced as a hollow performative tool that consisted of expressive rhetoric over substantive outcomes. This dissertation argues that the political marketing strategies that governed political campaigns were critical in the decision to adopt or reject penal populism as a campaign tool. By situating the presence or absence of penal populism within the context of shifting campaign strategies, this study deepens our understanding of Canadian punitive exceptionalism and shows that the Canadian version of penal populism was heavily influenced by advancements in political marketing strategies. This study also advances our understanding of the narratives used to shape a ‘tough-on-crime’ image and the extent to which it existed between electorally successful conservative campaigns. Through an analysis of the emotional rhetoric used to promote punitive sentencing policy, specifically mandatory minimum and maximum sentences, this dissertation exposes the manner in which emotionality is used to enhance the appeal of these political offerings.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.030 | 0.016 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".