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Record W2745533244 · doi:10.2014/igj.v50i1.1259

On Maitre D's, Trojan Horses and Aftershocks: Neoliberalism Redux in Ireland after the Crash

2017· article· en· W2745533244 on OpenAlexaff
Mark Boyle, Patricia Burke Wood

Bibliographic record

VenueIrish Geography · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsYork University
Fundersnot available
KeywordsNeoliberalism (international relations)AusterityCrashPolitical economyPeck (Imperial)ReduxScrutinySociologyScholarshipBrexitPoliticsPolitical scienceEconomicsLawEuropean unionEngineering

Abstract

fetched live from OpenAlex

Critical scholarship has revealed the darker side of the model of economic recovery, which Ireland has embraced from 2008 and has placed under scrutiny the claim that the country is witnessing a ‘Celtic comeback’ because of this model. But as crisis recedes and the contours of a new normal become manifest, perhaps it is surprising that less attention is being given to the politics of Ireland’s post-crash politico-institutional architecture and growth agenda. In this brief provocation, we mobilise Peck, Theodore and Brenner’s (2013) theorisation of ‘neoliberalism redux’ to explore the structuration of regulatory institutions and experiments in Ireland after the crash. We argue that whilst Ireland will continue to be cast as a small open, liberalised, entrepreneurial and glocalised economy, its post-crash development manifesto needs to be construed as less a straightforward reset or return to a pre-crash model after a shock or blip and more a historically novel and contested reimagining and reinvention. It could have been – and may yet be – different. We invoke the themes of ‘maitre d’s’, ‘Trojan horses’ and ‘aftershocks’ to open a debate on the forces which will combine to determine the fate of neoliberalism redux in Ireland.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.935

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)0.0000.000

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 head0.017
GPT teacher head0.224
Teacher spread0.207 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2017
Admission routes1
Has abstractyes

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