Multilingual Legal Text Authentication and Harmonization of Legal Terminology: Foreign Models and Prospects for Kazakhstan
Bibliographic record
Abstract
In the context of Kazakhstan’s integration into the international legal framework, the issue of ensuring linguistic authenticity of bilingual legal texts—officially published in Kazakh and Russian—gains particular importance. Discrepancies in meaning between the two language versions can negatively impact legal certainty, judicial consistency, and public trust in law. This study aims to comprehensively examine foreign models of legal text authentication and terminology harmonization and assess their applicability to Kazakhstan’s legislative process. Special focus is given to systems in Canada, Switzerland, Belgium, the EU and CIS countries. The relevance of this topic lies in addressing the institutional and linguistic barriers that hinder full equality between Kazakh and Russian in criminal law. The practical significance of the work is reflected in its development of specific proposals to improve Kazakhstan’s bilingual lawmaking, based on international standards. The research methodology is grounded in comparative legal analysis, examination of constitutional and sectoral acts, judicial practice, and academic literature. The study identifies effective institutional mechanisms and terminological strategies that support authenticity. It highlights the need for simultaneous bilingual drafting and enhanced linguistic review of legislative texts. A set of best practices and adaptation mechanisms for Kazakhstan is proposed. This research contributes to improving legal language quality and advancing the field of legal linguistics in Kazakhstan. The outcomes can be applied to prevent legal uncertainty arising from linguistic mismatches in legislation and to ensure equal interpretability of laws in both state languages.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".