Bibliographic record
Abstract
We introduce a dataset called the 'Yonkoma four-panel Cartoon Image' dataset. This dataset consists of both grayscale and color images of Yonkoma four-panel cartoons that were created between 1920 to 2022. The images were collected from a range of online sources, including digitized newspapers such as Chosun Ilbo (https://newslibrary.chosun.com/) and Dong-a Ilbo (https://www.donga.com/archive/newslibrary). We used the DarkLabel2.4 software to create bounding boxes for the Yonkoma four-panel images. Two types of formats were used for the boxes, which were 4x1 and 2x2. The labeling information for each image file was generated in the You Only Look Once (YOLO) format and saved in text files for object detection in deep learning. The dataset comprises a total of 161 image files in jpg format, which were randomly divided into training, validation, and testing sets using the Roboflow software.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.031 |
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".