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Record W4323074646 · doi:10.4000/monderusse.13939

Constructing the average worker’s family budget after Stalin

2023· article· en· W4323074646 on OpenAlexaff
Kristy Ironside

Bibliographic record

VenueCahiers du monde russe · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies on Reproduction, Gender, Health, and Societal Changes
Canadian institutionsMcGill University
Fundersnot available
KeywordsPledgeNormativePopulationGovernment (linguistics)Power (physics)EarningsSubsidyState (computer science)Political scienceWelfareSociologyEconomicsPublic administrationPolitical economyLawAccountingDemography

Abstract

fetched live from OpenAlex

The article looks at published “average” family budget studies in the Khrushchev era following the February 1956 Twentieth Party Congress and the Soviet government’s pledge to more fully meet the population’s material needs. Despite being portrayed as objective reflections of reality, harnessing the expert authority of statisticians and the power of numbers, as this article shows, these studies in fact featured highly idealized constructions of family composition and economic practices, as well as gendered assumptions about men’s and women’s contributions to the normative household budget. At the same time, published “average” family budget studies played an important pedagogical role during this time, helping to explain the benefits of shifts away from Stalin-era policies, such as encouraging Stakhanovite overproduction and bestowing routine retail price cuts on the population, and toward more abstract but, as it was argued, more tangibly beneficial policies like workers’ “invisible earnings” from the free or highly subsidized benefits of the Soviet welfare state.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.007
Scholarly communication0.0030.003
Open science0.0000.002
Research integrity0.0010.001
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 head0.032
GPT teacher head0.233
Teacher spread0.201 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

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