“ALLEGIANCE!”: LITERARY TRANSLATION OF REFERENCE NETWORKS IN LEACOCK’S COMIC SKETCHES
Bibliographic record
Abstract
My purpose in this article is to briefly discuss the usage of notes in my translation of Stephen Leacock’s (1869-1944) humorous novel Sunshine Sketches of a Little Town (1912), currently in progress. After setting forth the research problem and delineating some of the main features of my object’s overall and specific contexts, I reflect upon the foreignising status of my translation and test the plausibility of providing my Brazilian readers with a “hypertextual” version of the Canadian book, as I include information that is not available in the original. Alongside such debate, looking at literature as an unceasing flow of meanings and effects results inevitably in my accentuation of how liquefied the situation of those who take part within the process tends to be. Author, text, translator, reader: there are no concrete instances in the atmosphere of literature – they all blend and traverse one another in a ghostlike fashion, but we shall never be able to access and/or define with precision how such contact occurs. Translating, in this sense, in spite of resurrecting the original endeavours to make its ghost keep haunting other people.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".