THE LANGUAGE OF REGULATORY LEGAL ACTS: IS IT TIME TO SOUND THE ALARM?
Bibliographic record
Abstract
Introduction: the article describes the experience of assessing the readability of regulatory legal acts by analyzing the complexity of syntactic constructions used in the texts. According to the subjective perception, normative texts become more complicated from year to year, which makes it difficult to interpret them and understand the legal meaning. Purpose: to test this hypothesis based on metrics and, if confirmed, to formulate recommendations for simplification of legal texts. For this, the authors studied the methods used in Russia and across the world to assess the complexity of official texts and to simplify them. Methods: having not found suitable tools for assessing the readability of syntactically overburdened texts of regulatory legal acts, the authors applied their own assessment methodology based on machine analysis of syntax indicators. The investigation was conducted in relation to specially prepared corpora of texts: 12 corpora of all federal laws effective on different dates and a corpus of 3,390 by-laws. The study also compared the syntactic complexity of regulatory legal acts and texts of other categories (fiction, articles in the media, etc.). Results: the study proves that the degree of syntactic complexity of legal texts is significantly higher than that of texts of other styles; moreover, it increases with time. For example, federal regulations being in effect at the end of 2021 are by 33% more complex than those in force in 1991. Conclusions: the modern language of regulatory legal acts is excessively complicated. As a rule, the same content can be presented in a simpler manner. The review of the literature showed that the growing complexity of legal texts is a vital issue to address not only in Russia. To overcome the existing negative practice, administrative measures are required, such as the preparation of recommendations for the texts of draft regulatory legal acts and the expansion of the subject of linguistic assessment that such texts undergo.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".