A tradução para o português dos socioletos literários da trilogia Fundação, de Isaac Asimov
Bibliographic record
Abstract
In general, among all the themes studied in Science Fiction, the translation of linguistic varieties is one of the less regarded by the literary criticism. These varieties play an important role to the internal verisimilitude in their fictional universes. The so called “literary sociolects” can express many attributes of a character, their values and attitudes, as theorized by the Canadian researcher Lane-Mercier (1997). This research is mainly focused on the linguistic varieties of Isaac Asimov’s Foundation trilogy (Foundation, Foundation and Empire, and Second Empire) - a futuristic science fiction classic. The trilogy tells the story of how the psychohistorian Hari Seldon, with his mathematical and sociological predictions, could foresee the fall of the Galactic Empires and stablish a Foundation at the end of the galaxy in order to assemble all human knowledge and thus try to contain its total collapse. Based on a qualitative comparison between American English and Brazilian Portuguese from the Foundation's trilogy, topics such as deviations from translation norms and some deforming tendencies proposed by Antoine Berman (2007) were discussed. Therefore, analyzes of the most notable literary sociolects were carried out, among which: the religious and scientific technolects, the rural dialect of Narovi, the idiolectal ambiguity of the Mule, and both eye dialects of Lord Dorwin and Homir Munn. As a research corpus, there were used the most recent translation into Portuguese of the Foundation trilogy (2009 - Aleph), by Fábio Fernandes and Marcelo Barbão. Finally, a more general discussion of languages and linguistic varieties in other works of Speculative Fiction was presented, focused on Science Fiction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 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; both teacher heads 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".