Regulatory impact analysis: The experience of policy analysis in the Korean central government
Bibliographic record
Abstract
Introduction Policy analysis plays a crucial role in the policymaking process, contributing to rational decision making that makes public policy more efficient from policy formation all the way through to evaluation. The central government of Korea has been conscious of the importance of policy analysis since the 1960s, and first adopted policy analysis in 1967 alongside the Second Five-Year Economic Development Plan (1967– 71). The Economic Development Plan (EDP) was initially proposed based on the need for professional and systematic plans to tackle global economic challenges. The EDP was taken a step further through analysis and forecasts by participating foreign experts, ministries, financial institutions and other professionals under the supervision of the Economic Planning Board. However, the government-driven EDP ended in the late 1990s with the Asian financial crisis. Since then, a large chunk of analysis in the central government has given way to government-funded research institutes (Jung, 2002). Korea has adopted numerous key policy analysis systems since the late 1970s. The Environmental Impact Assessment, which began in 1977, was aimed at achieving balance between development and conservation, pursuing eco-friendly sustainable development, and creating a healthy and pleasant environment (Ministry of Environment, 2016). The Traffic Impact Assessment of 1987 was brought in to analyse and predict the effects of traffic volume, flow and safety in order to minimise traffic problems. The regulatory impact analysis (RIA) was introduced in 1998 to improve regulatory quality and curb the creation of unreasonable regulations (The Office for Government Policy Coordination, OGPC et al, 2005). The preliminary feasibility study was initiated in 1999 to review the economic and technological feasibility of large-scale government projects, to ensure the objectivity of feasibility studies and improve the efficiency of financial investment. Since the 2000s, the Gender Impact Assessment in 2002 was introduced to evaluate the socio-economic disparities between men and women in the process of establishing and implementing major policies in order to promote gender equality (Ministry of Gender Equality and Family, 2018). The Corruption Impact Assessment in 2005 was designed as an anticorruption policy by eliminating unclear laws and unrealistic regulations, promoting appropriate standards in the process of drafting and enacting laws, bringing greater transparency to administrative procedures and rationally analysing the causes of corruption (Anti-Corruption and Civil Rights Commission, 2017).
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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 | 0.040 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".