Bibliographic record
Abstract
Translating comics is as old as the comics genre itself.The dating of the translation activity thus depends on how one defines comics. 1 The definition adopted here is quite standard; a comics is: « a story using sequential images with words » (McCloud 1-23; Hague 11-18) The umbrella term "comics," therefore, includes comic strips, comic books, graphic novels, French bandes dessinées, Japanese mangas, but also Italian fumetti, Brazilian quadrinhos, Corean manhwas, and Chinese manhuas, to cite only the most famous types of comics in the globalized twentieth and twenty-first centuries.2 And yet, it is only very recently that academia has shown interest in the study of comics and comics in translation.In the 2000s, the number of university courses offered on comics or that included comics has exponentially risen in the world, especially in North America (Tabachnick).Although less frequently than in the U.S.A., some courses in France, taught in English departments, include comics taught in translation.It should be mentioned that the success of those courses tends to increase the demand and thereby the offer for more and more comics materials, and has an impact, albeit indirect, on the quality of published titles.This relatively new academic interest in the genre of comics can find its origin in the obvious comicsmania that has been spreading around the world in the last few years, a phenomenon that is boosted even more by blockbusters adapted from all kinds of comics from Superman to Satrapi's Persepolis and Clowes' Ghost World.In France, for instance, graphic novels like Batman Returns, Watchmen, and Maus sold very well in their translated French versions, while, in the U.S.A., Satrapi's Persepolis is very popular on many campuses in its English translation.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.679 | 0.574 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".