KRAZY! : the delirious world of anime + comics + video games + art
Bibliographic record
Abstract
Comics! Cartoons! Anime! Manga! Graphic novels! Video games! This vibrant and engaging book, catalog to a landmark exhibition, celebrates the variety and growing significance of visual pop culture. Stunningly illustrated with eye-popping art, Krazy! investigates the uniqueness of these forms while considering the ways they interconnect. Curated by many of the artists who first brought these forms to the public's attention, this volume features commentary and interviews with Maus author Art Spiegelman, SimCity creator Will Wright, and Canadian comic book author and illustrator Seth, along with Tim Johnson (codirector of Antz and Over the Hedge), Kiyoshi Kusumi (a global authority on manga), and media theory critic Toshiya Ueno. This pathbreaking volume crosses boundaries between the printed arts, films, and video games and analyzes the reciprocal influences between fields, highlighting the best of each. The energy and intensity of the images leap off every page, and the full experience of the exhibit itself comes alive in behind-the-scenes commentary by the contributors. Krazy! is a dizzying introduction to the art forms that will dominate the new century.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".