Understanding Peanuts and Schulzian Symmetry: Panel Detection, Caption Detection, and Gag Panels in 17,897 Comic Strips Through Distant Viewing.
Bibliographic record
Abstract
In this article, we applied distant viewing to a corpus of 17,897 comic strips from Charles Schulz’s _Peanuts_ as a primary case study. Distant viewing uses computational techniques to study large-scale visual media, and draws upon interdisciplinary areas including visual media studies, cultural studies, data science, and semiotics. We focus on comic strips, particularly _Peanuts_, due to their widespread readership, historical and cultural cache, and complexity as a medium built on the interplay between text, image, and meaning. First, we discuss previous work done at the intersections of comics studies and computer vision. Next, we establish the processes for applying computer vision to comic strips. After that, we provide several examples, including: panel detection (variations in panel length over a cartoonist’s career); caption detection (identification and location of captions in panels); and comics paratext (computer vision analyses/exclusions of copyright text, signatures, dates, etc.). Combined studies of panel detection, caption detection, and comics paratext reveals new insights into the success, longevity, and influence of one of the world’s most famous newspaper comic strips. Ultimately, computer vision reveals a subtle stability and symmetry to Schulz’s artistry that played an understudied but significant role in the comic strip’s popularity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".