Transposing Proper Names in Frank McCourt’s Memoir Angela’s Ashes from English into Maltese
Bibliographic record
Abstract
This paper focuses on the transposition from English into Maltese of the various proper names encountered in Frank McCourt’s memoirAngela’s Ashes(Chapter 1). To achieve this aim, an extended practical translation exercise by the author himself is used. Eight different categories of proper names were identified in the source-text ranging from common people names to nicknames, titles and forms of address. Four different categories of cross-cultural transposition of proper names were considered, although only two were actually used. Various translation strategies were adopted ranging from non-translation to modification, depending on whether the particular proper name has a ‘conventional’ meaning or a culturally ‘loaded’ meaning. Although cultural losses were unavoidable, cultural gains were also experienced. Wherever possible, the original proper names were preserved to avoid any change in meaning and interference in their functionality as cultural markers. Moreover, a semantic creative translation was preferred, especially with proper names that were culturally and semantically loaded to reduce the amount of processing effort required by the target-reader and to minimize the cultural losses of relevant contextual and cultural implications in the target-text.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".