Social and Legal Technologies in the System of Legal Policy
Bibliographic record
Abstract
The article considers the problem of implementing of legal policy as a social technology. The authors compare the concepts of social and legal technology as a set of elements in achieving the goal, and also consider systematicity as the main property of these technologies. The systematic approach is presented both at the decision-making level and at the stages of its legislative execution and practical application in the process of implementing of legal norms. The implementation of legal policy led to the dynamics of legislative changes in recent years. Various state institutions have been reformed and actually reorganized to work on the basis of new principles. Moreover, the reforms of recent years are determined not only and not so much by objective ideological transformations associated with the transition to democracy, the implementation of international law, but also by a change in the technological paradigm of management and implementation of political processes. The actions of the executive and legislative branches, as well as the entire process of legal proceedings in courts of various levels, are considered in the article as unique social technologies, all of which are systemic in nature. The authors conclude that the consistency of power, social and legal technologies serves as a vehicle for political legal strategy, and also allows you to express the functionality of the main legal institutions.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.064 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".