The Politics of Sex, Race and Working-Class Slang in Late Second Empire French Caricature
Bibliographic record
Abstract
This study examines caricatures as they appeared within popular Parisian magazines in mid-19th century France at the time of the 1867 World’s Fair. Chapters compare the comic mockery of several of the most important satirists of this time, including Amédée de Noé, or “Cham” (1818–1879) as he was more popularly known, and Honoré Daumier (1808–1879). A major theme within the analysis is how these caricaturists secretly used argot (street slang), as documented in two slang dictionaries by Parisian litterateur, Alfred Delvau (1825–1867), within their comic images to carry hidden encrypted messages in order to evade the censorship of the day. The book focuses primarily on caricatures of Chinese visitors who were part of the 1866 diplomatic visit to Paris and images of Chinese at the 1867 Exposition Universelle, showing how the satires which were published by Cham used argot to create highly sexualised images that were often racist in nature. In contrast, the volume proposes that Daumier used slang in his caricatures to challenge racism and to make secret reference to current political leaders and politics. The book will be of interest to scholars working in art history, visual culture, media studies, and communication studies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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".