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Record W2057262128 · doi:10.4337/roke.2012.01.07

Fiscal austerity, the Great Recession and the rise of new dictatorships

2012· article· en· W2057262128 on OpenAlexaff
Hassan Bougrine

Bibliographic record

VenueReview of Keynesian Economics · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsLaurentian University
Fundersnot available
KeywordsAusterityEconomicsProsperityRecessionPovertyDictatorshipDevelopment economicsGovernment (linguistics)Post-Keynesian economicsInequalityEconomic policyStructural adjustmentFiscal policyDemocracyMacroeconomicsPolitical scienceEconomic growthPoliticsMarket economy

Abstract

fetched live from OpenAlex

Austerity measures have been tested in developing countries for several decades under the pseudo name of ‘structural adjustment programmes’ following the recommendations and under the supervision of the World Bank (WB) and the International Monetary Fund (IMF). Evidence indicates that the economic and social consequences of these policies have been so disastrous that there is now more poverty and more inequality than a generation ago. The same scenario is being proposed as the alternative to what has hitherto been labeled a social-democratic economic system in industrialized nations. The economically dominant minority has largely succeeded in imposing its neoliberal agenda by convincing the general public, through various means, of the need for austerity. The paper challenges the erroneous theories on which austerity is based and proposes an alternative explanation to what is the ‘best practice’ in public finance. It argues that deficit spending by the government is an important policy tool that can be successfully used to guarantee full employment and create wealth and prosperity for the whole society.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.007
Scholarly communication0.0040.005
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.039
GPT teacher head0.246
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 source (direct Gemma or distilled Codex), 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

Citations9
Published2012
Admission routes1
Has abstractyes

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