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Record W4380624045 · doi:10.1111/1475-6765.12604

The electoral risks of austerity

2023· article· en· W4380624045 on OpenAlexafffund
Costin Ciobanu

Bibliographic record

VenueEuropean Journal of Political Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsMcGill University
FundersGovernment of CanadaMcGill University
KeywordsAusterityPower (physics)EconomicsEstimationSurvey data collectionDemographic economicsPolitical economyPolitical sciencePoliticsLaw

Abstract

fetched live from OpenAlex

Abstract Does austerity influence incumbent support? Existing studies struggle with conceptualizing the evolution of austerity's impact over time, estimating a causal effect, and analysing the reactions of different voters. This study theorizes that the effect of austerity on electoral preferences is not immediate, but gradual, as voters find out about the measures' consequences via the media. It leverages a survey in the field at the time of the austerity announcement in Romania in 2010, additional survey data collected immediately after this event and comprehensive daily media coverage to show that austerity measures do not have an immediate impact on incumbent support, anticipated turnout and expressing a vote preference. Instead, there is a gradual effect that is associated with increased media attention to budgetary cuts. This natural experiment allows the estimation of the immediate causal effect of austerity on electoral intentions. Difference‐in‐differences (DID) models show that the announcement triggered a massive loss of support for the incumbent among those who had voted for the party in power only a few months before. Austerity also led to the demobilization of the governing party's supporters. There is no evidence that those most directly affected by the spending cuts are more likely to punish the incumbent party.

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)0.014
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.791
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.593
GPT teacher head0.599
Teacher spread0.006 · 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.

Study designTheoretical or conceptual
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

Citations19
Published2023
Admission routes2
Has abstractyes

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