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Record W1973682237 · doi:10.1177/223386590200500102

Beyond the IMF Medicine: Thailand's Response to the 1997 Financial Crisis

2002· article· en· W1973682237 on OpenAlexaboutno aff
Shalendra D. Sharma

Bibliographic record

VenueInternational Area Review · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsRecessionGovernment (linguistics)EconomicsEconomic recoveryAdministration (probate law)Financial systemFinancial crisisQuarter (Canadian coin)Economic policyPolitical scienceKeynesian economicsLaw

Abstract

fetched live from OpenAlex

From the summer of 1997 through summer 1999, Thailand's economy took a steep plunge into recession and economic collapse. Only in the third-quarter of 1999, did the economy give signs of a fragile, if hesitant recovery. Almost immediately the International Monetary Fund(IMF) took credit, arguing that its policies and programs were finally producing results. How valid is this claim. This paper argues that the IMF policies by itself cannot explain the recovery. Indeed, if the Thai government continued to follow the IMF's orthodox prescriptions, it would have dragged the economy further into crisis. Rather, it is argued that the Chuan administration deserves much credit for the recovery. By rejecting some of the IMF's mis-guided policies and by its effective management of the crisis, the Chuan government laid the basis for a gradual and sustained recovery.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.041
GPT teacher head0.267
Teacher spread0.227 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

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