Bibliographic record
Abstract
The first issue of John Layman and Rob Guillory’s Chew was released in June 2009 by Image Comics at a time when the American comic book market was so dominated by stories written within the superhero genre that ‘comic books and superheroes [had] almost become synonyms’ (Rhodes 2008: 6). Within this superhero market, Chew was remarkably not a comic book about a superhero. Instead, Chew is a New York Times bestselling, Eisner award-winning series about Tony Chu, a Chinese- American cibopath. As a neologism created by the comic’s authors, cibopathy describes the ability to receive psychic impressions from whatever one eats. Although Chu has this extraordinary ability, he does not have a secret identity, a costume, an origin story or a mission to save the world from evil. Instead, Tony works as a detective for the American Food and Drug Administration (FDA) in a possible future where the FDA has become the most powerful government agency in the world. While the Department of Homeland Security enhanced the scope of police powers as a result of the catastrophic events associated with September 11 in our reality, the FDA has done the same in response to the devastating events associated with an avian flu epidemic in Chew’s alternate reality.
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 | 0.003 | 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 | 0.017 | 0.033 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".