Practice, Description and Theory Come Together – Normalization or Interference in Italian Technical Translation?
Bibliographic record
Abstract
This article aims at the characterization of specific features of translated texts. Taking a classroom experience as its starting point, the use of anglicisms in original and translated computing texts in Italian is examined. The corpus used for this purpose has three components: originals in Italian, comparable translations into Italian, and their English source texts. The frequency of three sets of English words – overt lexical borrowings, adapted borrowings and semantic loans, and morphosyntactic calques (plurals ending in –s ) – is compared across the monolingual comparable subcorpus components. The parallel subcorpus is then checked to disprove the null hypothesis according to which observed differences are unrelated to the translation process. The results of the quantitative analysis, followed by careful qualitative observations, confirms that translators are more conservative in their choices and normalize more than writers, who seem to be more prone to interference from English as the lingua franca of the IT discourse community. Implications at the methodological, descriptive/theoretical and applied levels are discussed.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".