“‘... but of these it is better that there remains no memory.’ The Connotation(s) of memory in the English translations of Se questo e un uomo”.
Bibliographic record
Abstract
The one and only English translation of Se questo è un uomo (1947) by Primo Levi was published for the first time in 1959, and made by Stuart Woolf, in close collaboration with the author himself. Despite the attempts by Primo Levi to have new translations of both Se questo è un uomo and La tregua over the years, Woolf’s translation is the only English translation available to this day, with the partial exception of the adaptation made by George Whalley for the Canadian Broadcasting Corporation, aired in 1965. The translation by Woolf and the adaptation by Whalley will be analysed following a corpus-assisted comparative approach. In particular, the results of the quantitative analysis of both target texts will be presented, while a qualitative analysis of the theme word memory and its related forms will be performed. The word memory was chosen in view of its broader semantic area, that encompasses the meanings related to both memoria and ricordo. The analysis of the extended co-textual referents of the word memory aims at providing further insights into its related non-obvious meanings as well as into its evaluative connotations.
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 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 | 0.002 |
| 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 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".