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Record W2046443497 · doi:10.1111/1467-8683.00282

Making a Successful Transition from a Command to a Market Economy: the lessons from Estonia

2002· article· en· W2046443497 on OpenAlexaff
James Gillies, Jaak Leimann, Rein Peterson

Bibliographic record

VenueCorporate Governance An International Review · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsYork University
Fundersnot available
KeywordsPlanned economyIndependence (probability theory)RealisationEconomyTransition economyPrincipal (computer security)Market economyEconomic systemWelfareEconomicsBusinessPolitical science

Abstract

fetched live from OpenAlex

It is now more than ten years since the dismantling of the USSR began, not only in terms of the reasserted independence of the member states, but also in terms of the end of the centralised Soviet command economy. The experiences of a myriad of investors, public and private, who have lost funds they so hopefully made available to emerging enterprises in the Eastern European states, particularly in Russia, have clearly demonstrated to Western observers the difficulties associated with making a transition from a command to a market economy. And yet the transformation in different states has had different degrees of success. For example, in Estonia, the strategies to move from a command to a market economy, adopted by the political leadership throughout the 1990s, have been relatively successful. In this article, the policies that led to this success are identified. It is believed that tentative generalisations from the experiences of Estonia may be helpful in determining the necessary conditions for successful change from a command to a market economy in other countries. To the extent that such transitions contribute to real economic growth, empirical evidence about the conditions necessary for their realisation provides general understanding of the forces underlying economic development. Moreover, it may well be that these generalisations can be used by private and public organisations to obtain a first indication of the probabilities of success of investments that they are contemplating making in a transitional economy, whether measured by return on capital, and/or contribution to general welfare.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.679
Threshold uncertainty score0.997

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.150
GPT teacher head0.370
Teacher spread0.219 · 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 designNot applicable
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

Citations3
Published2002
Admission routes1
Has abstractyes

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