On the problem of defining manga: A study about the influence of Taoism and Zen Buddhism on manga aesthetics
Bibliographic record
Abstract
Since the expansion of Japanese comic books throughout western countries, the so-called “manga style” has get attention from audiences and theorists. But how can we identify such Japaneseness? Trying to fulfill readers` interests, books have been published under the how-to-draw-manga label, usually highlighting the visual composition of characters, from clothes to facial expressions to hairstyle. From the academic perspective, particularities of page layout have been also considered since Pierre Fresnault-Deruelle`s idea of tabularity. Such structuralist perspective is also echoed by contemporary scholars such as Benoît Peeters and Thierry Groensteen. Investigations on what is called the “grammar of mangas” were also proposed by Neil Cohn or Scott McCloud (or at least based on his contributions). But what are they referring to by “manga”? Artists from all around the world translate mangas into transnational experiences. This study proposes a wider understanding of the manga narrative style and its particular aesthetic influence on readers. The study focuses on the Asian philosophies of Tao and Buddhism, identifying how their ideals are articulated to promote reader’s immersion in the narrative. The article investigates the visual representations of the Taoist idea of vacuum and the Zen idea of trivia, which characterize the visual and narrative fluidity of manga – especially those whose stories are based on everyday life.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".