Contextualizing Trumpism: Understanding Race, Gender, Religiosity, and Resistance in Post-Truth Society
Bibliographic record
Abstract
From within the discipline of religion and culture studies, this thesis contextualizes the intersecting discourses surrounding race, gender, and religion underpinning “Trumpism” as an exclusionary populist rhetoric in the United States with similar trends emerging in Canada, Europe, and parts of the Global South. In the US, Trumpism represents not only the political style and rhetoric of its namesake, but the mentality of a distinct voter base compelled to “make America great again.” Pressurized by contemporary social realities and a sensationalist media culture, Trumpian rhetoric can be understood as a “whitelash” response to changes in the American social fabric enmeshed in a cultural history of (white) Christian nationalism. To better understand the cultural and political undertones embodied by Trumpism, this research project presents four Focused Cultural Examples (FCEs) to engage critical discourse/media analysis in dialogue with academic literature. Each FCE examines an event or cluster of topics at the intersections of race, gender, and religion, including antithetical political movements and counter-narratives which challenge and resist Trumpism and what it represents. The synthesis chapter includes brief Canadian comparisons and considers some strategies for building more equitable and informed communities.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.045 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".