Translatability of Law and Legal Technology – Findings from Corpus Analyses and Bilingual Legal Drafting in Canada
Bibliographic record
Abstract
Lawyers and citizens increasingly engage with law through technology intermediaries. For example, to declare their taxes, they consult tax software rather than the tax code. This greater role of legal technology raises new issues for bilingual jurisdictions. In Canada, for instance, federal legislation is not translated, but simultaneously co-drafted by Francophone and Anglophone lawyers resulting in small differences in the expression of the law and occasional inconsistencies. This contribution showcases how these differences can affect legal technology applications. Depending on the language they work with, lawyers may encode different interpretations in software and algorithm may yield different results. Using a bilingual corpus of 3000 Canadian federal regulations as a case study, we demonstrate that the same artificial intelligence techniques applied to the same legal texts in different languages yield different results. As a consequence, we argue that legal technology cannot simply be developed for one language and then translated to another language. Instead, legal technology has to be “co-developed” for different languages similar to how legislation is currently “co-drafted.”
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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".