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Record W3198154496 · doi:10.5267/j.ijdns.2021.7.004

The analysis of factors affecting the household savings as a part of food security management

2021· article· en· W3198154496 on OpenAlexvenueno aff
Gelena Pruntseva, Stepan A. Davymuka, Valentyna Yakubiv, Taras Vasyltsiv, Iryna Anhelko, Inna Irtyshcheva, Yuliia Maksymiv, Iryna Hryhoruk, Rostyslav Bilyk, Nazariy Popadynets

Bibliographic record

VenueInternational Journal of Data and Network Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture Market Analysis Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsPurchasing powerFood securityBusinessGovernment (linguistics)Investment (military)PurchasingAgricultureEconomicsPublic economicsEconomic growthPoliticsMarketing

Abstract

fetched live from OpenAlex

Ensuring household food security should be a priority goal of state policy. The level of ensuring household food security reflects the state of the country's economic development and the effectiveness of agricultural policy. Household food security is achieved by ensuring a high level of purchasing power of households, which is possible by increasing income. Savings are the “safety cushion” for households during the financial and economic crisis caused by the coronavirus pandemic. The level of household savings is important both for the households themselves and for the country's economy, since savings, on the one hand, help to avoid hunger during crises, and, on the other hand, are an important investment resource for the country's economy. That is why assessing the level of household savings and identifying factors affecting savings are important aspects of building an effective government policy in the field of food security.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.260
Teacher spread0.234 · 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 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

Citations12
Published2021
Admission routes1
Has abstractyes

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