The Stabilization of the Regulatory Burden: The "One-In, One-Out" Principle Implementation Challenges
Bibliographic record
Abstract
The principle “one-in, one-out” means that the regulator should abolish the existing regulation in the amount of the costs imposed by a new regulation on business. This article contains an overview of experience in applying the “one-in, one-out” principle in Great Britain, Canada, and Australia. This research provides an analysis of the existing models of its application their features and quantitative results of the application of the principle, with the most interesting part being statistical data on the magnitude of costs, aimed at fulfilling the requirements of legislation which could reduce the overall burden of legislation on business during the application of the ne-in, one-out” principle. B Basing on the study of international experience, an analysis of risks relating to the use of this principle, is carried out. The implementation of the principle «one-for-one" is shown in lawmaking activities in Russia: the regulatory framework governing its use, as well as the results of the first enforcement. An analysis of the practice of application of the principle in Russia is based on statistics provided on regulation.gov.ru. portal. The research results in drawing conclusions about opportunities and consequences of the principle "one-for-one" implementation that can be claimed when applied in Russia. At present, the principle does not embrace all draft regulations that may increase the administrative burden. The formulating of the principle in legislation brings about some ambiguity that creates uncertainty in using it, as well as in methods (limitations on using the standard cost model). The practice of canceling the existing regulation, based on the «one-in, one-out» principle, is rare. However, the application of the principle in certain areas may have a positive impact on the reduction of administrative costs, such as the revision of the sectoral regulatory and legal framework of state control, the formation of a unified register of reporting forms and review of business reporting to public authorities.
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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 | 0.069 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.013 | 0.020 |
| 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".