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Record W4404982569 · doi:10.1080/08911916.2024.2412471

Debt Reduction for Economic Resurrection and Redistribution

2024· article· en· W4404982569 on OpenAlexaff
Murray Bryant, Guðrún Johnsen, Gylfi Magnússon, Þröstur Olaf Sigurjónsson

Bibliographic record

VenueInternational Journal of Political Economy · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsWestern University
Fundersnot available
KeywordsRedistribution (election)EconomicsDebtKeynesian economicsMacroeconomicsPolitical sciencePolitics

Abstract

fetched live from OpenAlex

In October 2008, Iceland faced a severe crisis when its financial system collapsed, leading to economic, social, political, and international turmoil. The currency plummeted, asset prices fell, inflation surged, real wages declined, and unemployment soared, sparking public unrest. Citizens lost trust in institutions and each other as blame for the crisis spread widely. In response, successive governments implemented innovative policies to aid recovery and restore public trust. This paper examines the debt policies adopted during the crisis and the rapid adjustments made as new information emerged. Policymakers experimented with various tools, some of which deviated from traditional IMF guidelines but were later adopted by the IMF in severe crises. International relations were strained, notably in the Icesave dispute, where Iceland was listed alongside terrorist organizations by a long-time ally. Unlike other countries, Iceland focused on supporting households and productive firms rather than bailing out financial institutions, offering a model for alternative crisis response. Many of Iceland’s policies resembled the Global South’s but with unique measures, such as significant debt reduction and enhanced social safety nets. This paper emphasizes that addressing household and corporate debt was crucial for successful redistribution efforts.

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 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: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.355

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 head0.020
GPT teacher head0.285
Teacher spread0.265 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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