Bibliographic record
Abstract
The cities of Montréal, Nantes, Dandenong and Barcelona diverge in several ways from patterns of consolidation in austere neoliberalism discussed in Chapter 4. This chapter first discusses Montréal and Nantes, as two cases of established urban regimes dealing with policy failure and coming under strain from internal contradictions. The second part of the chapter explores Greater Dandenong and Barcelona, cities with very different political orientations and traditions, but where constructive regime building activities were occurring, respectively at a distance from and against austere neoliberalism. Chapters 2 and 3 contextualised the discussion of regime politics in Montréal, explaining the policy of rigueur at the provincial level, measures to centralise political and administrative control and the economic development dilemmas facing the city in recent decades. The government of Québec is a regime agenda setter, in the dual sense of controlling key public service budgets and, through austerity measures, downloading problems to other actors in the statutory and third sectors (Hamel and Autin, 2017). Hamel and Keil (2020) emphasise that rigueur was not only about cuts, but a state restructuring project amounting to a revanchist attack on the collaborative, democratic and deliberative traditions of the city. Between 2013 and 2017, the city of Montréal was governed by an electoral coalition, Équipe Denis Coderre, which took a pragmatic stance towards Couillard’s agenda.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.054 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".