‘Enabling dissent’: Contesting austerity and right populism in Toronto, Canada
Bibliographic record
Abstract
Abstract Following the 2008 financial crisis, austerity measures have been introduced in many national contexts to reorganise public sector work and redesign labour laws and labour policies. At the same time, right-populist discourses and movements have arisen in ways that give both legitimacy and voice to the politics of austerity. Toronto, Canada, provides a world-renowned case of populist experimentation at the metropolitan scale, as the actions of Mayor Rob Ford typified this nexus of austerity and populism. Set in the context of Ford’s term as Mayor of Toronto (2010–2014), this article asks how the combined rise of austerity and right populism creates both new challenges and new opportunities for public sector labour in urban spaces. We argue that public sector unions are central in both the making and unmaking of populist austerity and identify potential trajectories for organised labour in the face of the continuation of austerity-driven politics.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".