Bibliographic record
Abstract
Abstract Asian American graphic narratives typically produce meaning through arrangements of images, words, and sequences, though some forgo words completely and others offer an imagined “before” and “after” within the confines of a single panel. Created by or featuring Asian Americans or Asians in a US or Canadian context, they have appeared in a broad spectrum of formats, including the familiar mainstream genre comics, such as superhero serials from DC or Marvel Comics; comic strips; self-published minicomics; and critically acclaimed, award-winning graphic novels. Some of these works have explicitly explored Asian American issues, such as anti-Asian racism, representations of history, questions of identity, and transnationalism; others may feature Asian or Asian American characters or settings without necessarily addressing established or familiar Asian American issues. Indeed, many works made by Asian American creators have little or no obvious or explicit Asian American content at all, and some non-Asian American creators have produced works with Asian American representations, including racist stereotypes and caricatures. The earliest representations of Asians in comics form in the United States were racist representations in the popular press, generally in single-panel caricatures that participated in anti-immigration discourses. However, some Asian immigrants in the early to mid-20th century also used graphic narratives to show and critique the treatment of Asians in the United States. In the realm of mainstream genre comics, Asian Americans have participated in the industry in a variety of different ways. As employees for hire, they created many well-known series and characters, generally not drawing, writing, or editing content that is recognizably Asian American. Since the 2010s, though, Asian American creators have reimagined Asian or Asian American versions of legacy characters like Superman and the Hulk and created new heroes like Ms. Marvel. In the wake of an explosion of general and scholarly interest in graphic novels in the 1990s, many independent Asian American cartoonists have become significant presences in the contemporary graphic narrative world.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 teacher head, 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".