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A Review of Marina Balina, Sergei Oushakine (eds.), The Pedagogy of Images: Depicting Communism for Children. Toronto; Buffalo, NY; London: University of Toronto Press, 2021, XX+548 pp.

2024· review· en· W4399932543 on OpenAlexaffabout
Olga Boitsova

Bibliographic record

VenueAntropologicheskij forum · 2024
Typereview
Languageen
FieldArts and Humanities
TopicVisual Culture and Art Theory
Canadian institutionsECW Press (Canada)University of Toronto
Fundersnot available
KeywordsCommunismProletariatIdeologyPoliticsCriticismArt historyHistoryMedia studiesSociologyVisual artsArtLiteraturePolitical scienceLaw

Abstract

fetched live from OpenAlex

The reviewed book The Pedagogy of Images: Depicting Communism for Children, edited by Marina Balina and Sergei Oushakine, is dedicated to Soviet children’s book illustrations of the 1920s–1930s, which had an ideological function. The introduction and sixteen chapters written by different authors demonstrate a variegated picture in which there is a place for avant-garde artistic experiments, educational projects and discussions about children’s books. Illustrations for books that were not related to politics did not come into the focus of attention of the authors of the collection, but nevertheless the coverage of the material is very wide. Different chapters examine how paper, nature, electricity, vezdekhodnost (goeverywhereness), time, the death of Lenin, the Red Army, the proletariat, and “Americanism” were represented in children’s illustrations. Due to involvement of many researchers, the book presents different approaches and methods of visual analysis borrowed from visual studies, art criticism, and history. Not all the authors are convincing in their analysis, but the publication of this collection is undoubtedly an important event in this field of study, even though illustrations for children of the 1920s and 1930s are well-studied.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.642
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.039
GPT teacher head0.327
Teacher spread0.287 · 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.

Study designNot applicable
Domainnot available
GenreReview

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 routes2
Has abstractyes

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