{"id":"W4401104901","doi":"10.54373/ifijeb.v4i3.1584","title":"Visualizing Humans Contributions to Tax Complience Research: A Bibliometric Analysis","year":2024,"lang":"en","type":"article","venue":"Indo-Fintech Intellectuals Journal of Economics and Business","topic":"Taxation and Compliance Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Taxpayer; Compliance (psychology); Multidisciplinary approach; Field (mathematics); Accounting; Business; Public economics; Political science; Psychology; Economics; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.00586835,0.0005734316,0.0008380641,0.1253591,0.001542273,0.008359924,0.0005461898,0.0006793782,0.003779343],"category_scores_gemma":[0.0301097,0.0002303059,0.0008426041,0.1298288,0.0009598164,0.004551242,0.002964959,0.0003873572,0.0005892161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001720546,"about_ca_system_score_gemma":0.002444842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005341943,"about_ca_topic_score_gemma":0.006834489,"domain_scores_codex":[0.9935613,0.002194333,0.0008778474,0.0005459794,0.002534024,0.0002865127],"domain_scores_gemma":[0.9722806,0.01825776,0.003738978,0.001280481,0.00392224,0.0005199194],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003521692,0.000155577,0.3216549,0.005968275,0.001033012,0.0006684935,0.02905757,0.003521318,0.005775694,0.02615589,0.01921454,0.5864425],"study_design_scores_gemma":[0.00007262665,0.0002720042,0.6528404,0.002872129,0.001322275,0.001692595,0.07194915,0.01784607,0.005320739,0.02790058,0.2176706,0.000240838],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8488804,0.02443345,0.02078066,0.004840581,0.0002797545,0.000549642,0.02073125,0.001392615,0.07811172],"genre_scores_gemma":[0.9562432,0.009005735,0.02499931,0.00009238697,0.0001971016,0.0003116393,0.006318998,0.0001223694,0.002709257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9941316,"threshold_uncertainty_score":0.03103513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1682150542986889,"score_gpt":0.373121396834525,"score_spread":0.2049063425358361,"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."}}