Bibliographic record
Abstract
Sus trabajos de investigación versan sobre la relación entre imagen y texto en la traducción, sobre todo en los cómics.Actualmente está elaborando su tesis doctoral cuyo título es O conhecimento do outro por meio da imagem e da tradução.En 2012 obtuvo la Maestría en Letras con la tesis Imagem e texto em tradução: uma análise do processo tradutório nas histórias em quadrinhos.Participó de diversos congresos nacionales e internacionales, siendo uno de ellos el Colloque du 60e anniversaire de Meta 1955-2015, con la presentación y publicación del trabajo L'image marquée par la culture: une refléxion sur les marqueurs culturels dans la bande dessinée.También publicó artículos en revistas académicas: "O corpo fala, mas em que língua?: o gesto e a fala na tradução de quadrinhos" en Artefactum, e "Ils sont fous ces traducteurs: considerações sobre a tradução do humor em Astérix" en In-Traduções, ambos publicados en 2014.También es autora, junto con su tutora, Adriana Zavaglia, del artículo "Histórias em quadrinhos: imagem e texto em tradução" publicado en Tradterm en 2010. Érico Gonçalves de Assis is currently pursuing his PhD in Translation Studies degree inUniversidade Federal de Santa Catarina.His main research interest is comics translation, with a focus on comics translation lettering.He has been
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 | 0.004 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.669 | 0.394 |
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".