Winking, allusions, and anagrams: Translating character names in Harry Potter
Bibliographic record
Abstract
In Harry Potter, J.K. Rowling presents to readers a foreign world in the familiar setting of England with a wide array of characters who have a wide variety of names. Harry Potter is challenging for translators who must translate character names while maintaining a balance between the foreign and the familiar, as does Rowling. Rowling’s work is identified as a piece of ‘kiddult’ literature where it is directed at both adults and children. Additionally, Rowling is described as winking at adults through her use of allusions in character names. Some translators argue in favour of localizing all character names in the target language while others argue for the direct transfer of names from English to the translation. This paper argues in favour of a balance between localization and direct transfer (or globalization) in the translation of texts like Harry Potter. After a comparison of the literature, a translation model is presented to aid translators in establishing a balance between localization and direct transfer by providing specific situations in which it is advisable to translate character names and when it is advisable to directly transfer character names into the translation.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; a candidate call from one teacher head, 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".