Bilingual and Multilingual Legal Dictionaries: New Standards for the Future
Bibliographic record
Abstract
Alarmed by the notorious inaccuracy of “traditional” bilingual and multilingual legal dictionaries, legal lexicographers began experimenting with new methods of improving user reliability about 15 years ago. Analyzing numerous bilingual and multilingual legal dictionaries of various languages (combinations of English, French, German, Spanish, Italian, Dutch and Chinese), the author claims that one can now speak of a special methodology of legal lexicography which has set new standards for the future. Focusing on the problems of interlingual transfer in the field of law, the author deals with the problem of equivalence, pointing out that, in the majority of cases, the functional equivalents of different legal systems are only partially equivalent. This has led to the need to measure the degree of their equivalence in order to determine their acceptability in dictionary entries. For this purpose, methods of comparative conceptual analysis can be used. Moreover, bilingual legal dictionaries are now equipped with a more or less elaborate documentary apparatus including definitions of both the source term and its equivalent, contextual data and geographic information on the usage of target language variants. In conclusion, the question is raised as to the role of dictionaries in the standardization of legal terminology at the national level (Canada), the regional level (EEC, CMEA) and at the international level (UN).
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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 | 0.056 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.018 | 0.030 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".