{"id":"W4409288162","doi":"10.31292/wb.v5i1.236","title":"Optimalisasi Pajak Progresif sebagai Instrumen Penertiban Tanah Terlantar: Studi Komparatif dan Rekonseptualisasi Kebijakan Pertanahan di Indonesia","year":2025,"lang":"en","type":"article","venue":"Widya Bhumi","topic":"Economic Growth and Fiscal Policies","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"WiLAN (Canada)","funders":"","keywords":"Political science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005218762,0.0005392135,0.001068004,0.0005887123,0.000433413,0.0003264626,0.0008555956,0.0002795847,0.0001120848],"category_scores_gemma":[0.0001173927,0.0005965502,0.0003051454,0.0006018771,0.0003603715,0.0005435974,0.0002873363,0.0004075925,0.0003767694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002858492,"about_ca_system_score_gemma":0.0001012696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009730532,"about_ca_topic_score_gemma":0.0006068719,"domain_scores_codex":[0.9965106,0.00006690609,0.001368083,0.001017891,0.00008303755,0.0009535397],"domain_scores_gemma":[0.9980977,0.00008724661,0.0004846364,0.000983236,0.00005665943,0.0002905741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008341805,0.0002497222,0.6904824,0.00009517247,0.0002361105,0.00001610184,0.001879416,0.00004452955,0.00006574616,0.2922236,0.0129018,0.00172199],"study_design_scores_gemma":[0.003137943,0.0004208376,0.7542878,0.000156439,0.00009923733,0.00002767708,0.002420891,0.002755425,0.0009752233,0.01283181,0.2210734,0.001813254],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8763056,0.002003789,0.0002320863,0.003706987,0.000847379,0.0006158313,0.000145476,0.0001849445,0.115958],"genre_scores_gemma":[0.9931932,0.0001371815,0.0003181098,0.001686033,0.0002749977,0.0001568136,0.0001263501,0.00006486151,0.004042425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2793918,"threshold_uncertainty_score":0.9996486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02412926288709385,"score_gpt":0.2361734017165993,"score_spread":0.2120441388295055,"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."}}