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Record W4384697717 · doi:10.1177/00220094231186089

Food Waste and Survival in Times of the Soviet Famines in Ukraine

2023· article· en· W4384697717 on OpenAlexaff
Iryna Skubii

Bibliographic record

VenueJournal of Contemporary History · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsQueen's University
Fundersnot available
KeywordsFood wasteAgricultureState (computer science)RationingFood systemsFood chainEconomyAgricultural economicsEconomic growthGeographyNatural resource economicsFood securityEconomicsEcologyArchaeologyBiology

Abstract

fetched live from OpenAlex

This article focuses on the interconnections and interrelations between food, waste, people and state during a series of survival crises in the famines of 1921–3, 1932–3 and 1946–7 in Soviet Ukraine. Owing to grain and food requisitions, the collectivization of agriculture and rationing, as part of the state's growing control over the flow of economic resources from the 1920s to the 1940s, discarded food acquired particular importance for people's survival during these times of extremes. Focusing on both individual and institutional levels of waste production and regulation, this study explores the role of food waste in the survival practices of the starving and traces the development of their individual resourcefulness and interconnectedness with wider social and natural environments. The article explores different types of food waste, including husks, leftover food, carrion and spoiled and rotten food and the spaces of its collection. By ‘following’ the traces of waste in urban and rural landscapes, including, among others, dumpsters, slaughterhouses, cattle cemeteries and railway stations, the article brings into focus the critical changes in human–food, human–waste and human–nature relationships in times of extremes.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0020.001
Open science0.0000.004
Research integrity0.0010.001
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 head0.034
GPT teacher head0.217
Teacher spread0.183 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Contemporary HistorySame topicFood Waste Reduction and SustainabilityFrench-language works237,207