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International Financial Institutions and Market Liberalization in the Developing World

2016· book-chapter· en· W2516632504 on OpenAlex
Stephen C. Nelson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOxford University Press eBooks · 2016
Typebook-chapter
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsScience North
Fundersnot available
KeywordsDeveloping countryLiberalizationOpenness to experienceStructural adjustmentInternational economicsEconomicsBusinessFinancial systemEconomic policyDevelopment economicsMarket economyEconomic growth

Abstract

fetched live from OpenAlex

Abstract This article examines the role played by the two most important international financial institutions (IFIs), the World Bank and the International Monetary Fund (IMF), in the developing countries’ transition towards market liberalization and openness. More specifically, it considers whether IFIs are powerful “globalizers” of the developing world or ineffective organizations whose grand plans are forever thwarted by savvy governments promising sweeping reforms that never materialize. Drawing on the findings from thirty-one recent empirical studies, it concludes that there is no clear evidence that the IFIs’ conditional lending has significant effects on structural reforms in developing countries. Nevertheless, the chapter argues that we should not regard the IFIs as completely useless agents in the effort to remake developing countries’ economies over the past thirty years, suggesting that their indirect effects on liberalizing policy reforms may be more important than the direct effects.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.980
Threshold uncertainty score0.379

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 communication0.0000.000
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 head0.038
GPT teacher head0.252
Teacher spread0.214 · 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