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Record W1979616266 · doi:10.1002/sres.2291

A System Dynamics Modelling of Contagion Effects in Accounts Risk Management

2014· article· en· W1979616266 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSystems Research and Behavioral Science · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAccounts receivableFinancial institutionEconomicsDomino effectInstitutionSystemic riskFinancial contagionPaymentFinancial marketMonetary economicsFinancial economicsFinancial crisisFinanceMacroeconomics

Abstract

fetched live from OpenAlex

Financial contagion has been with us as long as there has been an economy. The system of collective human behavior usually creates stable markets, but occasionally, this collective behavior results in various bubbles. Financial contagion specifically deals with the domino effect of one banking institution failing, which, as a result of interrelationships with other banks, leads to further failures. The year 1929 was a very bad year, but 2008 had its moments as well. These financial contagions result in undermining confidence in similar institutions. Our research question is to examine whether the role of accounts receivable payments is affected by social interaction of those holding loans from a lending institution. System dynamics modelling is used to demonstrate the impact of word‐of‐mouth social contacts on accounts receivable, and the ensuing increase in financial risk. Copyright © 2014 John Wiley & Sons, Ltd.

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.

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)0.008
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.834
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.070
GPT teacher head0.318
Teacher spread0.248 · 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