Los certificados de antecedentes penales en Reino Unido, Estados Unidos, Canadá, Australia, Nueva Zelanda y España.: Análisis contrastivo aplicado a la traducción
Bibliographic record
Abstract
In the last few years, Spanish law firms’ workload\nand foreign business activity in Spain have increased.\nThe number of English-speaking immigrants in the Peninsula\nhas also substantially risen. These events make\nthe English<>Spanish translation of Police and Crime\nLaw documents in general and criminal records in particular\nof utmost importance. Nevertheless, we have noticed\nthat this topic has never been researched to date.\nThat is the reason why we analyzed and compared 70\ncriminal records, real translation briefs from 2012 and\n2013, 10 from Spain, 10 from England, Wales and Northern\nIreland, 10 from Scotland, 10 from USA, 10 from Canada,\n10 from Australia and 10 from New Zealand. Our\naim was to identify several divergences and facilitate\nthe translation task; both direct (English-Spanish) and\nreverse (Spanish-English), of the text typology originated\nfrom this branch of Crime Law. Our findings show\nstriking differences after an interlinguistic and an intralinguistic\nanalysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".