Austerity, Competitiveness and Neoliberalism Redux: Ontario Responds to the Great Recession
Bibliographic record
Abstract
This article examines the deepening integration of market imperatives throughout the province of Ontario. We do this by, first, examining neoliberalism’s theoretical underpinnings, second, reviewing Ontario’s historical context, and third, scrutinizing the Open Ontario Plan, with a focus on proposed changes to employment standards legislation. We argue that contrary to claims of shared restraint and the pressing need for public austerity, Premier McGuinty’s Liberal’s have re-branded and re-packaged core neoliberal policies in such a manner that costs are socialized and profits privatized, thereby intensifying class polarization along with its racialized and gendered diversities. Cet article analyse l’intégration de plus en plus profonde des impératifs du marché dans la province de l’Ontario. Nous faisons cette analyse, premièrement, en analysant les bases théoriques du néolibéralisme, deuxièmement, en décrivant le contexte historique de l’Ontario, et troisièmement, en examinant le “Open Ontario Plan”, sous l’angle particulier des propositions de changement de la législation sur le droit du travail. Nous soutenons que sous le couvert de discours prônant le partage de l’austérité et l’impérieuse nécessité de restreindre les dépenses publiques, les Libéraux du Premier McGuinty ont ré-étiqueté et reformulé les politiques néolibérales de façon que les coûts soient socialisés et les profits privatisés, aggravant ainsi la polarisation des classes ainsi que les inégalités liées à la race et au genre.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".