Bibliographic record
Abstract
The long-discussed – and frequently dismissed – concept of translation faithfulness or translation fidelity, though usually applied to literary texts, has its fair share of applications when considered for comics translation. In literary translation, non-linguistic portions such as illustrations are often considered addenda or “paratexts” relative to the main, linguistic text. Comics, by its turn, present a certain set of features which single them out as a form that demands a new concept of “text” and, therefore, of translation fidelity. The comic-reading process, as pertaining to cognitive apprehension, implies interpretative accords that differ from the ones in purely linguistic texts: each and every element of the comics page – non-linguistic (mainly imagetic) signs, linguistic signs, panel borders, typography and such – are intertwined and should be perceived in regards to its spatial and topological relations. This approach to understanding comics is based on Groensteen (1999) and his concepts of arthrology, spatio-topia, page layout, breakdown and braiding. As for translation fidelity, we rely on authors such as Berman (1984), Guidere (2010) and Aubert (1993). On comics translation, Zanettin (2008), Rota (2008) and Yuste Frías (2010, 2011) are of particular interest. Based on various concepts of fidelity – supported by samples of translated comics with varied degrees of fidelity to the source text – we discuss the different grounds of source-text fidelity, target-reader fidelity and source-author fidelity in the following instances: linguistic sign fidelity, imagetic sign fidelity, spatio-topia fidelity, typographic fidelity and format fidelity.
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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".