Bibliographic record
Abstract
Comic art scholarship has finally gotten a foothold in the academy, after decades of individual and short-term efforts. A number of reasons can be ventured for this hesitancy to study comic art, including academic snobbery and protection of disciplinary turfs, and lack of grants, organized research collections, and other resources. Those who pioneered comic art scholarship were often fans, collectors, aficionados, and cartoonists, who researched from their personal collections. A substantial amount of the early research in the 1960s and 1970s was done in France, Italy, Spain, Germany, Sweden, England, Japan, and, to a lesser extent, China and the United States. A few individuals also recorded the histories of Australian and Canadian comic books. The stories of these pioneering efforts are full of interesting anecdotes. More organized academic research has resulted since the 1990s. Reasons for this were that the academy could not continue to ignore popular culture (and comics) because of its importance; comics were reinvented as a more sophisticated medium; a theoretical framework evolved, and graduate students felt safer embarking on the writing of dissertations based on comic art.
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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.019 | 0.045 |
| Scholarly communication | 0.035 | 0.028 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.025 | 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".