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Record W3093891313 · doi:10.29173/topo26

Seizing the Means of Domesticity: Mass-Housing Spaces, Objects and Relationships in Soviet Everyday Life

2020· article· en· W3093891313 on OpenAlexvenueno aff
Steven Shuttle

Bibliographic record

VenueTopophilia · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsnot available
Fundersnot available
KeywordsEveryday lifeState (computer science)UncannyModernization theorySociologyAestheticsFetishismEthnographyRealmPolitical scienceArtLawAnthropology

Abstract

fetched live from OpenAlex

The Soviet state created mass-housing to reshape the city and everyday life itself. This paper examines the spaces and objects of mass-housing to examine the relationship between residents, the state and objects within Soviet everyday life. Approaching the study of everyday life in the Soviet Union from the early 1920s to the late 1980s via the spaces of mass-housing can offer a tangible approach to a way of life that might otherwise seem strange or uncanny. This paper uses ethnographic analysis by drawing on scholarly sources along with five historical photographs. The mass housing spaces of the kommunalka and later khrushchyovka served as places of push and pull. The state attempted to expand the public realm while residents simultaneously tried to create privacy and individuality. Within the interior, the Red Corner and the commode were embodiments of contradictions between modernization and tradition. Despite the state’s efforts, commodity fetishism lingered at the core of everyday life. Within Soviet everyday life, mass-housing spaces and objects can be useful to illustrate the changing yet stagnant relationship between residents and the 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 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)0.002
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 score0.303
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.069
GPT teacher head0.280
Teacher spread0.211 · 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 teacher head, 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
Published2020
Admission routes1
Has abstractyes

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