Trumping Canada? Continuity And Change In Canadian Conservative Campaign Rhetoric
Bibliographic record
Abstract
The Conservative Party of Canada has been widely noted for its meticulous branding and tight message control. In contrast, US president Donald Trump, representing the traditionally conservative Republican Party, demonstrates a remarkable lack of message discipline: his infamous unscripted candor often descends into vulgarity. Yet, despite his lack of message discipline, Trump was successfully elected president, suggesting that his distinctive rhetorical style may have contributed to his electoral appeal. This major research paper explores whether Donald Trump’s surprising victory may have inspired Canadian Conservatives to alter their own rhetorical strategies in the hopes of achieving similar success. I conducted a qualitative rhetorical analysis on six campaign speeches delivered by Conservative Party leaders in Canada’s two most recent federal elections (Andrew Scheer in 2019 and Stephen Harper in 2015). The results suggest that the Conservatives’ campaign speech rhetoric does not appear to be converging with Donald Trump’s. However, further investigation into other sites of discourse, such as leaders’ debates, press conferences, or party documents, may reveal otherwise—particularly when it comes to broader ideological orientation and the treatment of minority groups
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.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.035 | 0.013 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".