{"id":"W4392159205","doi":"10.46692/9781447362593.007","title":"Regulatory impact analysis: The experience of policy analysis in the Korean central government","year":2023,"lang":"en","type":"other","venue":"","topic":"E-Government and Public Services","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Government (linguistics); Policy analysis; Central government; Business; Political science; Public administration; Local government","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0397309,0.0006217847,0.0004763581,0.002393915,0.008285107,0.0150588,0.002315702,0.003302399,0.006153625],"category_scores_gemma":[0.01452089,0.0005128596,0.0007197586,0.007026369,0.01035489,0.007584265,0.007052079,0.007145228,0.0009144354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02364321,"about_ca_system_score_gemma":0.03498031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02179209,"about_ca_topic_score_gemma":0.01831045,"domain_scores_codex":[0.9820859,0.01050218,0.0009054563,0.001034438,0.002969773,0.002502143],"domain_scores_gemma":[0.9796274,0.009946536,0.001670574,0.001357308,0.005059732,0.002338502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002053702,0.0006299136,0.015407,0.001395085,0.0001163515,0.002510304,0.1018038,0.009968051,0.001484951,0.5763512,0.06338648,0.2267414],"study_design_scores_gemma":[0.00005885129,0.0002131494,0.01057151,0.001639544,0.00007641139,0.0005456417,0.1143481,0.005860682,0.002336317,0.06607497,0.7980842,0.0001906158],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3301937,0.02306182,0.02356952,0.1293441,0.001315897,0.0006892168,0.0006169364,0.0004479758,0.4907608],"genre_scores_gemma":[0.9427116,0.01145739,0.009484494,0.007708846,0.0002238838,0.000197589,0.0002792187,0.000224137,0.02771296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0397309,"threshold_uncertainty_score":0.2101195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01502209971030194,"score_gpt":0.3249373510546164,"score_spread":0.3099152513443145,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}