The effective usage of corpora in legalese studies (on the example of the fiducie)
Bibliographic record
Abstract
If we consider corpus linguistics as the study of a language through its samples, we should give credit to its contribution to the advancement of various sub-fields of linguistics: lexicography, translation studies, applied linguistics, diachronic studies and contrastive linguistics. The latter can be regarded as a special case of a linguistic typology that is distinguished from other types of typological approaches by a small sample size and a high degree of granularity (Gast 2011: 2-3). Nowadays, corpus-based contrastive studies can be treated as a growing research area that focuses on two or more languages. The present paper makes an attempt to discuss the usefulness of the specialized combined parallel-comparable corpus while dealing with legalese. The effectiveness is presented on the example of the legal institution fiducie. The methodology of research comprises the comparative analysis as well as the corpus-based analysis of the terms related to the fiducie-s presented in three varieties of French: France’s, Canadian and Luxembourgish. The carried out research reveals the juridical-semantic differences and the problematics of the verbal realization of the concepts related to three fiducie-s. These hinder a proper interpretation and complicate the process of translation. The major solution is found through the specification of meaning by renaming.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".