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.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".