Exploring the Discursive Construction of a Populist Social Movement: A Case Study of Take Back Alberta
Bibliographic record
Abstract
During the COVID-19 pandemic, scholars, policymakers, and citizens alike observed an increase in polarization due to the global rise of misinformation (information disorder) and populist discourse in both online and offline spaces; Canada and Alberta are no exception. This inductive, qualitative case study explores Take Back Alberta (TBA), an Alberta-based social movement that emerged in response to COVID-19 health mandates. At its peak, TBA mobilized thousands of Albertans to oust Jason Kenney, elect a ‘freedom-minded’ premier, and prevent Alberta’s NDP from becoming a majority government. The primary research question asks: As a populist social movement, what discursive and organizational strategies does Take Back Alberta employ to influence politics in Alberta? Using multiple datasets, including semi-structured interviews with TBA supporters, content analysis of TBA speeches and meetings, desk research, and TBA’s social media pages, I review the material through a social movement theory lens, conducting thematic and frame analysis. TBA discursively constructed political leaders like Jason Kenney, Rachel Notley, and Justin Trudeau as enemies of ‘democracy’ and threats to Alberta’s prosperity. TBA leadership framed citizens as apathetic and portrayed the future as an existential crisis should the NDP win, successfully inspiring thousands of Albertans to utilize Institutional Opportunity Structures (IOS), a concept established during this research, to impact Alberta’s political landscape in a manner that supports illiberalism, not democracy for everyone.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.040 | 0.025 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 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".