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Record W4310738128 · doi:10.18280/ijsdp.170708

Features of the Formation and Transformation of Household Credit Behavior Under Macroeconomic Instability

2022· article· en· W4310738128 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
FundersMinistry of Education and Science of Ukraine
KeywordsEconomicsPolitical instabilityEconomic stabilityMacroeconomicsPoliticsPolitical science

Abstract

fetched live from OpenAlex

Within the article, the formation and transformation of credit behavior of households in the conditions of macroeconomic instability is examined. The research was conducted on the basis of the analysis of the features of economic development and the development of lending to households in Ukraine in 2006-2021. At the same time, the justification of theoretical features of the change in the specified type of behavior was carried out. First of all, in the article, economic and political conditions in which economy of Ukraine developed during the outlined period are studied. Appropriate statistical indicators were used for this purpose. Also, in the article, taking into account the indicators of macroeconomic dynamics, a thorough study of the field of lending to households was carried out. This made it possible to describe the peculiarities of the change in the state of such crediting in different conditions of the country's economic development. A detailed analysis of individual parameters of bank lending to households made it possible to describe the formed model of credit behavior of these economic entities and conduct a statistical analysis of the impact of individual economic parameters on changes in the volume of such loans, establish the strength of their influence and importance for ensuring stable functioning of the lending sphere of these economic entities.

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)0.001
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 score0.564
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.027
GPT teacher head0.234
Teacher spread0.207 · 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