Bibliographic record
Abstract
Drawing on a corpus of eight translations of Lewis Carroll’sAlice in Wonderlandinto five languages (German, French, Spanish, Brazilian Portuguese, Italian), the paper discusses the forms and functions of proper names in children’s books and some aspects of their translation. In Alice in Wonderland, we find three basic types of proper names: names explicitly referring to the real world of author and original addressees (e.g.,Alice, her catDinah, historical figures likeWilliam the Conqueror), names implicitly referring to the real world of author and original addressees (e.g.,Elsie,LacieandTillie, referring to the three Liddell sisters Lorina Charlotte, Alice and Edith Mathilda), and names referring to fictitious characters. An important function of proper names in fiction is to indicate in which culture the plot is set. It will be shown that the eight translators use various strategies to deal with proper names and that these strategies entail different communicative effects for the respective audiences.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".