Right-wing populism in a metropolis: Personal financial stress, conservative attitudes, and Rob Ford’s Toronto
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Elections such as the UK “Brexit” referendum, Donald Trump’s ascendancy to the presidency, and the growth of the Alternative for Germany party in Germany have led to concerns about the viability of liberal democratic institutions. Voters appear increasingly drawn to populists. However, before Brexit, and before Trump, there was Toronto Mayor Rob Ford. Known equally for bizarre personal antics and outsider status, Ford is a classic case of a right-wing populist politician. We examine this relatively early manifestation of a populist by taking into account various factors. One dominant theoretical explanation is based on economic anxieties amidst increasing inequality and polarizing labor markets. A second, and perhaps more dominant theory, emphasizes working class xenophobia and racism. Results from an analysis of a 2014 survey suggest support can be explained by many factors, such as ideology, partisanship, social conservatism, education, financial stress, suburban residency, among others. Sometimes, factors show a direct link to support for Ford. In other cases, particularly as it relates to financial stress, the relationship is more complex. Also, analysis shows that visible minorities were more likely than non-visible minorities to support Ford, contrary to the general anti-immigrant and sometimes racist appeals of populists elsewhere.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it