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Record W2306508669 · doi:10.1177/1035304616628409

‘Enabling dissent’: Contesting austerity and right populism in Toronto, Canada

2016· article· en· W2306508669 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Economic and Labour Relations Review · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of CanadaYork University
KeywordsAusterityPopulismPoliticsPolitical economyLegitimacyDissentPolitical scienceContext (archaeology)Neoliberalism (international relations)Public administrationSociologyLawGeography

Abstract

fetched live from OpenAlex

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.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.232
Teacher spread0.212 · 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