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Record W2335840126 · doi:10.34989/sdp-2016-6

The Role of the International Monetary Fund in the Post-Crisis World

2021· preprint· en· W2335840126 on OpenAlexaff
Mark Kruger, Robert Lavigne, Julie McKay

Bibliographic record

VenueEconstor (Econstor) · 2021
Typepreprint
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsBank of Canada
Fundersnot available
KeywordsRatificationPolitical scienceHumanitiesLawPhilosophy

Abstract

fetched live from OpenAlex

The International Monetary Fund (IMF, or the Fund) has undergone a number of significant policy changes and reforms in the wake of the global financial crisis. Most notably, in December 2015, the United States approved long-delayed legislation to increase the representation of developing countries in the Fund’s governance structure. The vital progress on quota shares has finally allowed for a resumption of wider and increasingly critical discussion of the strategic role of the IMF in the post-crisis world. This paper aims to relaunch the debate by assessing the recent reforms and changes, identifying areas where progress is still needed and proposing solutions. Our findings suggest that, while much has been accomplished by the Fund’s management and staff since the global crisis, there is still a pressing need for member countries to push for further reforms if the IMF is to remain a relevant player in the rapidly evolving global economic and financial system. Emerging-market economies remain under-represented at the Fund and continue to perceive the IMF as biased against them, undermining the influence of its advice, despite the increase in their quota share and changes to improve the quality, efficiency and even-handedness of the IMF’s surveillance and lending. In advanced economies, where the Fund has traditionally had little traction on national policies, the institution faces the challenge of managing and communicating its independence in programs involving large shareholders. We propose reforms aimed at improving country representation, granting the IMF real operational independence and enhancing its catalytic role.

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)0.010
metaresearch head score (Gemma)0.022
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0030.005
Scholarly communication0.0220.011
Open science0.0010.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.263
Teacher spread0.247 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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