The Defeat of Stephen Harper: A Case Study in Social Movement Electoralism
Bibliographic record
Abstract
This article aims to identify the key electoral dynamics of the 2015 federal election in Canada, an election which ended 10 years of Conservative rule and propelled the Liberals to office, under new leader Justin Trudeau. It suggests, building on the work of Rosa Luxemburg, that social movement electoralism—motivated by a strong anti-Tory sentiment—best captures the phenomenon of millions of new voters arriving at the ballot box. Using comparative data at a national, regional, and riding level, the article posits that the 2015 election witnessed an unprecedented increase in voter turnout, an increase that disproportionately benefitted the Liberals. Activist-driven strategic voting can only account for a small proportion of this turn to the Liberals, and the article introduces a Hypothetical Strategic Voting Test to make this point. The social movement electoralism framework has implications not just for our understanding of this and future elections, but for our understanding of the nature of contemporary democracy itself.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.015 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 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".