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Record W2954084903 · doi:10.5430/rwe.v10n1p60

The Role of the International Monetary Fund After the 2008 Crisis

2019· article· en· W2954084903 on OpenAlexvenueno aff
Marco Mele, Floriana Nicolai

Bibliographic record

VenueResearch in World Economy · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsCurrencyExchange rateChinaBusinessForeign exchange marketFinancial systemFinancial crisisEconomicsCorporate governanceInternational economicsFinanceMonetary economicsPolitical scienceMacroeconomics

Abstract

fetched live from OpenAlex

The purpose of this paper is to analyze the changes in the functions of the International Monetary Fund after the 2008 financial crisis. Following an extensive introduction concerning the subject of the study and which covers part of the economic literature, the focus was on governance reform and surveillance in the foreign exchange market. Finally, the empirical analysis was carried out concerning the manipulation of exchange rates in a period ranging from 2008-2016 and 15 countries (Taiwan, South Korea, Israel, China, Thailand, Macao, Switzerland, Hong Kong, Singapore, Norway, Qatar, United Arab Emirates, Kuwait, Trinidad and Tobago and Saudi Arabia) that in the period considered massively intervened in the foreign exchange market, keeping their respective currencies undervalued and acquiring an unfair competitive advantage to the detriment of partner economies. The results would tend to confirm that the manipulation of the exchange rate is a persistent and lasting element of the currency policies of the new millennium, highlighting an active insufficiency of the IMF’s action in the exercise of the oversight function on the currency policies of the Members.

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)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0070.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.045
GPT teacher head0.287
Teacher spread0.242 · 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 designTheoretical or conceptual
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

Citations1
Published2019
Admission routes1
Has abstractyes

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