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Record W2299557763 · doi:10.1057/9781137005236_5

The Electoral Impact of the 2008 Economic Crisis in Europe

2013· book-chapter· en· W2299557763 on OpenAlexaff
Lawrence LeDuc, Jon H. Pammett

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2013
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsRecessionBailoutEconomicsEconomic recoveryEuropean debt crisisFinancial crisisEuropean unionEconomic policyGlobal recessionEconomyInternational economicsEuropean integrationKeynesian economics

Abstract

fetched live from OpenAlex

The severe economic crisis that unfolded in Western economies in 2008 could be expected to have had political consequences as well as economic ones. As European economies slipped into recession in the latter part of 2008, the focus of attention in both European Union and national politics turned increasingly to economic matters. The immediate cause of the recession was widely attributed to external shocks, particularly the financial crisis in the United States, which was precipitated by events such as the collapse of the Lehman Brothers investment bank, the bailout of the insurance conglomerate AIG, and the ripple effects throughout the economy of those events. For most European economies, the low point was reached in the second quarter of 2009, with the average net growth in gross domestic product (GDP) for the EU27 at that time registering −4.2 per cent (Table 4.1). Only Poland escaped recession conditions, showing weak growth at an annualized rate of +1.7 per cent in this period. By the first quarter of 2010, all European countries had begun at least a modest recovery from the recession. However, this recovery began to stall as a second economic crisis took shape in Europe, involving sovereign debt markets in Greece, Spain, Ireland, and some other countries, generating pressures on European banks and other institutions and even raising anxieties about the potential survival of the Euro. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.009
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.024
GPT teacher head0.295
Teacher spread0.271 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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