Supplemental Information for: Overcoming the laws-in-translation problem: Comparing techniques to translate legal texts
Bibliographic record
Abstract
The benefits of computerized translations are their speed, accessibility, and cost. The risk is whether they are sufficiently precise for a given need. This note assesses the options available to translate legal text for socio-legal research. We evaluate three tools—DeepL, Google, Microsoft—and assess each one’s ability to translate similar legal content enacted by the Brazilian, Chinese, French, Japanese, and Mexican governments. We demonstrate that machine translators are reliable and effective, particularly at higher levels of generality. They are fallible, however, and each is prone to making critical errors that may jeopardize research. We show that employing human translators to edit automated translations produces high-quality translations in one-third the time and at a fraction of the cost. This methodological contribution promises to enrich socio-legal research by establishing a translation protocol that is affordable, rigorous yet simple, and transparent. We propose that scholars use this method for comparative socio-legal research.
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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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.794 | 0.290 |
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".