Framing and Temporality in Political Cartoons: A Critical Analysis of Visual News Discourse*
Bibliographic record
Abstract
Les caricatures politiques constituent une forme visuelle du discours des médias. Les sociologues rejettent généralement leur valeur idéologique en raison du fait qu'elles offrent aux lecteurs des exposés absurdes des conditions du « problème » putatif et ne doivent pas être prises au pied de la lettre. Toutefois, c'est par l'humour que les caricatures se sont emparées du bon sens et l'ont renfoncé, et par conséquent ont permis au public de classifier, d'organiser et d'inter‐préter activement ce qu'ils perçoivent ou vivent relativement à l'actu‐alité dans le monde à un moment donné de façon significative. Dans le cadre des théories interactionnistes de Goffman et de Mead, deux caricatures illustrant la dernière « crise » des « vagues d'immigration » déferlant au Canada seront étudiées. Political cartoons are a form of visual news discourse. Sociologists normally dismiss their ideological import on the grounds that cartoons simply offer newsreaders absurd accounts of putative “problem” conditions and are not likely to be taken very seriously. Nevertheless, it is through comedic conventions that cartoons seize upon and reinforce common sense and thus enable the public to actively classify, organize and interpret in meaningful ways what they see or experience about the world at a given moment. Informed by the interactionist theories of Goffman and Mead, two cartoons illustrating the recent “crisis” of “migrant waves” to Canada will be examined.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".