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Record W4400779983 · doi:10.22148/001c.118495

Soviet View of the World. Exploring Long-Term Visual Patterns in “Novosti dnia” Newsreel Journal (1945-1992)

2024· article· en· W4400779983 on OpenAlexvenueno aff
Mila Oiva, Tillmann Ohm, Ksenia Mukhina, Mar Canet Solà, Maximilian Schich

Bibliographic record

VenueJournal of Cultural Analytics · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicItalian Fascism and Post-war Society
Canadian institutionsnot available
Fundersnot available
KeywordsSoviet unionPeriod (music)Visual cultureWorld War IITerm (time)Political scienceHistoryMedia studiesVisual artsSociologyLawPoliticsArtAesthetics

Abstract

fetched live from OpenAlex

Newsreels, short documentary news films, were an influential channel of mass communication and propaganda in the Soviet Union. They served as an important means of visualizing the world for audiences in the way the Soviet authorities wanted it to be depicted. Studies in Soviet visual culture have recognized both continuities of repeating patterns and changes in the post-World War II period. This understanding is based primarily on temporally limited source selections, while a more systematic study of the developments in Soviet visual culture over a longer period is pending. In this article, we reveal long-term continuities, subtle changes, and sudden shifts in the official visual discourse in the Soviet newsreel series ‘Novosti dnia’ (News of the Day) 1945 to 1992. We study visual patterns in approximately 1,700 digitized newsreel issues, each about ten minutes long, using multidimensional vector embeddings. These embeddings, produced from the central frames of 205,678 shots, help visually evaluate the footage and assess visual similarities based on ResNet50 feature vectors. For this, we use the _Collection Space Navigator_ tool. The article demonstrates how multidimensional vector embeddings can be used to study the internal time of the films, and the external time of the years running by.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.169
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.066
GPT teacher head0.351
Teacher spread0.285 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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