{"id":"W4408256239","doi":"10.5267/j.dsl.2024.12.011","title":"Evolution and gaps in data mining research: Identifying the bibliometric landscape of data mining in management","year":2025,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"King Faisal University","keywords":"Bibliometrics; Data mining; Data science; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.01182687,0.0001065083,0.0001588856,0.03369233,0.0002955762,0.0008919958,0.00433186,0.00003305141,0.00002537654],"category_scores_gemma":[0.002074855,0.00007951823,0.00001015154,0.1035015,0.0004766541,0.00555909,0.007468004,0.0001788,0.00001650283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003812936,"about_ca_system_score_gemma":0.00003507094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004263678,"about_ca_topic_score_gemma":0.0003136948,"domain_scores_codex":[0.9970841,0.00003079462,0.0004590591,0.0008339148,0.001179068,0.0004130671],"domain_scores_gemma":[0.9973691,0.0006221085,0.0001353689,0.001737144,0.0001256273,0.00001068521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00006347673,0.00007407879,0.4528228,0.0002782786,0.000007318887,0.00004066789,0.0001230776,0.0001643129,0.002379615,0.002101489,0.04788595,0.4940589],"study_design_scores_gemma":[0.0004257634,0.000002417236,0.891232,0.001195183,0.00001518321,0.000002035552,0.003568447,0.09061003,0.0000362948,0.001084735,0.01166054,0.0001674075],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9804595,0.0007046287,0.01314002,0.003565702,0.0004399966,0.0002652446,0.000009661078,0.00001360026,0.001401673],"genre_scores_gemma":[0.9956452,0.0001679816,0.003391775,0.0006690607,0.00005957773,0.000005784743,0.00003400584,0.000005280844,0.00002134194],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4938915,"threshold_uncertainty_score":0.9772599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3616358362529741,"score_gpt":0.4476337674619509,"score_spread":0.08599793120897675,"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."}}