Diagnosing Doug Ford’s Durability: The Discourse and Political Economy of Right-Wing Populist Environmental Politics in Ontario
Bibliographic record
Abstract
As we approach the 2022 Ontario provincial election, political observers are apt to be somewhat confused. To most, it would seem that the current Ontario government, facing increasingly low popularity and widespread dissatisfaction with its management of the COVID-19 pandemic, has been ‘mugged by reality.’ However, as of yet polls show the Conservative Party of Ontario, (though with a dented reputation), very likely to retain power if an election were called today. This poses something of a theoretical dilemma. How do we make sense of an approach to governance that seems to have been discredited by reality, but shambles on relatively undisturbed in the discursive/political realm? With the goal of answering that question, this paper forwards a theory of the Ford government’s discursive strategy in general, and then examines how that style has persisted. It approaches this investigation using through discourse analysis, political-economic analysis, and a Gramscian analysis of hegemony. It proposes that the Ford government’s resilience can be attributed to the ability of its populistneoliberal and promethean-populist discourses to absorb and explain challenges accompanying COVID-19, changes in environmental politics, and labor market polarization in Ontario, as well as the inability of institutional discursive alternatives to provide a compelling counterhegemonic discourse that moves beyond the facilitative-managerial discourse the Ford government displaced in 2018. It concludes by suggesting that a revision of the ‘Green New Deal’ discourse that incorporates elements of deliberative democracy and a ‘green economic survivalism’ discourse might prove to be a more successful counter-hegemonic discourse.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.040 | 0.043 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".