{"id":"W4402445033","doi":"10.14738/assrj.119.2.17405","title":"Forensic Accounting: Exploration of Trends and Theme via Bibliometric Analysis","year":2024,"lang":"en","type":"article","venue":"Advances in Social Sciences Research Journal","topic":"Knowledge Management and Technology","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Theme (computing); Accounting; Data science; Management science; Computer science; Economics; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.01903967,0.00008955484,0.0002902439,0.1585635,0.0007274182,0.001032376,0.00106332,0.00007028225,0.0003053844],"category_scores_gemma":[0.001869903,0.00005994443,0.0001298791,0.4245267,0.001687737,0.003964818,0.0003569098,0.0004538861,0.00001822498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006677219,"about_ca_system_score_gemma":0.00008582416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001546452,"about_ca_topic_score_gemma":0.0003585567,"domain_scores_codex":[0.9951878,0.0003795424,0.0006051442,0.0004258203,0.00292963,0.0004720787],"domain_scores_gemma":[0.9977732,0.001350925,0.0001692994,0.0001304072,0.0005213456,0.00005481647],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000006611565,0.00001957086,0.04503598,0.000004166133,0.00002325812,0.00001151344,0.0005520143,0.00005234627,0.0000605526,0.01788132,0.0005328387,0.9358198],"study_design_scores_gemma":[0.0001949726,0.0002511176,0.04637394,0.0000219856,0.0000329624,0.00001022064,0.005720798,0.01533188,0.0001514482,0.8792074,0.05256059,0.0001426909],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7502553,0.05219511,0.07497834,0.01056051,0.001457875,0.0002693371,0.00000768918,0.00008528247,0.1101906],"genre_scores_gemma":[0.9965211,0.001885503,0.0006594797,0.000007740532,0.0001951286,0.000005484757,4.839268e-7,0.000003706007,0.0007214454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9356772,"threshold_uncertainty_score":0.9955229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3052672451296318,"score_gpt":0.5571467751815147,"score_spread":0.2518795300518828,"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."}}