{"id":"W3186244197","doi":"10.5430/rwe.v12n4p17","title":"Government Size and Corruption: A Non-linear Analysis in the Case of EMCCA","year":2021,"lang":"en","type":"article","venue":"Research in World Economy","topic":"Fiscal Policy and Economic Growth","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Language change; Government (linguistics); Economics; Panel data; Linear relationship; Econometrics; Public economics; Monetary economics; Development economics; Macroeconomics; Statistics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002888305,0.0001024831,0.0004492104,0.000452544,0.00007367862,0.00007070806,0.0002095604,0.00005568533,0.0004298842],"category_scores_gemma":[0.0001735049,0.0001070662,0.0001075684,0.001522745,0.0001377516,0.0001890917,0.0001519368,0.0003549634,0.00007648647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002324501,"about_ca_system_score_gemma":0.00003649018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001373782,"about_ca_topic_score_gemma":0.007117689,"domain_scores_codex":[0.9983751,0.00009072347,0.0006928488,0.0004138037,0.00003489562,0.0003926209],"domain_scores_gemma":[0.9986104,0.0007335035,0.0001269612,0.0004334319,0.0000205769,0.00007506353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004335772,0.0002989414,0.7443377,0.0001312711,0.0001978204,0.0007198976,0.001408191,0.0005212511,0.000004633307,0.2506049,0.0006218919,0.001110131],"study_design_scores_gemma":[0.002695858,0.0001314529,0.6252233,0.00006437905,0.00003085392,0.0001773979,0.00439413,0.06774438,0.0001741757,0.2603275,0.03846567,0.000570814],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8015304,0.0005403268,0.0000294443,0.003654941,0.00003786334,0.0001942518,0.00007881386,0.000002461527,0.1939315],"genre_scores_gemma":[0.9985389,0.00009459625,0.0001816246,0.0002428311,0.00007758641,0.00005611033,0.000004762122,0.000008794886,0.0007948123],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1970084,"threshold_uncertainty_score":0.470693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09029069921338073,"score_gpt":0.3281803398533912,"score_spread":0.2378896406400105,"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."}}