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Record W7095526580

Identity and the Effect of Ideational and Ideological Preferences on Monetary and Macroeconomic Policy: An Examination of the Former Eastern Bloc Countries1 Paper Presented to the Canadian Political Science Association annual meeting

2007· article· en· W7095526580 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicOral History, Memory, Narrative Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCzechMonetary policyIdeologyDivergence (linguistics)Convergence (economics)PoliticsAccessionGovernment (linguistics)European union
DOInot available

Abstract

fetched live from OpenAlex

Following the fall of communism, many expected that there would be a continuing convergence of economic policies amongst the post-communist Eastern European states (e.g., Hansson and Sachs, 1992). Since then, however, there has been a wide divergence in the economic orientations of the post-communist states, particularly in regards to monetary policy. Nowhere were these variances better illustrated than with the two historic accessions in 2004 and 2007 of large numbers of the post-communist Eastern European states to the European Union (EU). When examining the monetary policies of the post-communist states of Eastern Europe, four distinct groupings have emerged. The first, represented by Estonia and Czech Republic, has consistently made fundamental macroeconomic reforms to orient their monetary policies closer to Western Europe, which greatly facilitated each ones accession to the EU, and has put both on track to eventually meet EMU requirements. Relative to other post-communist states, Estonia and Czech Republic have been on the leading edge in terms of enhancing central bank independence, lowering inflation, and reducing government deficits. A second group, represented by Romania, which was initially slow in instituting macroeconomic reforms, has more recently increased its rate of monetary policy adjustments to

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)0.003
metaresearch head score (Gemma)0.001
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 score0.574
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.015
GPT teacher head0.256
Teacher spread0.241 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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