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Record W4285479849 · doi:10.51952/9781529210934.ch005

Regime Divergence and the Limits of Austere Neoliberalism

2021· book-chapter· en· W4285479849 on OpenAlexaboutno aff
Jonathan S. Davies

Bibliographic record

VenueBristol University Press eBooks · 2021
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNeoliberalism (international relations)Divergence (linguistics)Political sciencePolitical economySociologyPhilosophy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.974
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.264
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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