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Record W2945167469 · doi:10.3390/jrfm12020080

Money as an Institution: Rule versus Evolved Practice? Analysis of Multiple Currencies in Argentina

2019· article· en· W2945167469 on OpenAlexvenueno aff
Georgina M. Gómez

Bibliographic record

VenueJournal of risk and financial management · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsInstitutionCurrencyLiberian dollarEnforcementEconomicsPsychological resilienceMonetary systemDigital currencyMonetary economicsInstitutional economicsMonetary policyPolitical scienceFinanceLawKeynesian economics

Abstract

fetched live from OpenAlex

Monetary policies and adjustments during a financial crisis depend on policy-makers’ conceptions on what money is and how it works. There is sufficient consensus among scholars that money is an institution created within the economic system and is in line with other institutions that regulate economic action. However, there are different understandings of what institutions are and how they operate, and these understandings imply differences in terms of monetary enforcement, resilience, responsiveness and stability. This paper discusses the two main approaches that conceptualise institutions as rules and as practices presenting an empirically informed discussion of money as an institution drawing on these insights. It grounds the analysis on the empirical case of Argentina as a monetary laboratory and the plurality of currencies that circulate in its economy. The study argues that while the official currency of Argentina corresponds to the institutions as rules approach, the adoption of the U.S. dollar into a bimonetary economy evolved as equilibrium. In between, the massive community currency systems that rose and declined during the economic meltdown between 1998 and 2002 were a hybrid institution that combined rules and practice. All three of them show various degrees of resilience and stability.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.248
Teacher spread0.228 · 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 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

Citations15
Published2019
Admission routes1
Has abstractyes

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