Bibliographic record
Abstract
Donald J. Trump’s journey to the White House signaled the resurgence of right-wing populism in the United States. His campaign and his surprising electoral victory rode a wave of anti-elitism and xenophobia. He masterfully exploited the economic and cultural anxieties of white working class and petite bourgeois Americans by deflecting blame for their woes onto the “usual suspects,” among them minorities, liberals, Muslims, professionals and immigrants. His rhetoric touched a chord, and in fact emboldened and energized white supremacist ideologies, identities, movements and practices in the United States and around the world. Indeed, the Trump Effect touched Canada as well. This paper explores how the American politics of hate unleashed by Trump’s right-wing populist posturing galvanized Canadian white supremacist ideologies, identities, movements and practices. Following Trump’s win, posters plastered on telephone poles in Canadian cities invited “white people” to visit alt-right websites. Neo-Nazis spray painted swastikas on a mosque, a synagogue and a church with a black pastor. Online, a reactionary white supremacist subculture violated hate speech laws with impunity while stereotyping and demonizing nonwhite people. Most strikingly, in January 2017, Canada witnessed its most deadly homegrown terrorist incident: Alexandre Bissonnete, a right-wing extremist and Trump supporter, murdered six men at the Islamic cultural centre of Quebec City. Our paper provides an overview of the manifestations of the Trump Effect in Canada. We also contextualize the antecedents of Trump’s resonance in Canada, highlighting the conditions for and currents and characteristics of right-wing extremism in Canada.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.040 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".