Chewing in the name of justice: the taste of law in action
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it