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Record W3049247109 · doi:10.5430/rwe.v11n4p72

Price Transmission Among the Participants of the Livestock Products Agrofood Chain in Ukraine

2020· article· en· W3049247109 on OpenAlexvenueno aff
Olha Kravchenko, Віталіна Антощенкова, Larysa Batiuk, Veronika Lysenko

Bibliographic record

VenueResearch in World Economy · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture Market Analysis Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsLivestockProduct (mathematics)Production (economics)Distribution (mathematics)AgricultureEconomicsChain (unit)Agricultural economicsEconometricsIndustrial organizationBusinessMicroeconomicsGeography

Abstract

fetched live from OpenAlex

The article, by the example of livestock products, estimates and analyses the mechanism of price transfer (transmission) among the participants of the agrofood chain (agricultural producers, food processing and trading companies) and discovers abnormalities, imbalances and mismatches of the economic relations development among the stated participants. Straight-line regression with application of least-squares method was built, as well as three linear trend models that allowed to forecast the trends for price change at each administrative and technical stage of the product distribution (production, processing, trade). Quantitative evaluation was held for the price change of the livestock product market, and the time needed for the change. The obtained data confirmed the market “weakness” of agricultural goods producers denying the industrial safety guarantee of the country as a whole.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.287
Teacher spread0.200 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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