{"id":"W135208616","doi":"","title":"Bayesian Structural Equation Models for Cumulative Theory Building in Information Systems.","year":2012,"lang":"en","type":"article","venue":"Journal of the Association for Information Systems","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Structural equation modeling; Latent variable; Bayesian probability; Proxy (statistics); Reuse; Item response theory; Context (archaeology); Statistical model; Econometrics; Information theory; Machine learning; Data mining; Artificial intelligence; Mathematics; Statistics; Engineering","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005754593,0.0001375249,0.0003164856,0.0005377293,0.0002058807,0.0004437728,0.0005734239,0.0001413284,2.006818e-7],"category_scores_gemma":[0.001073372,0.000100665,0.0002154054,0.0005129558,0.000008263295,0.02315921,0.00006090525,0.0001580564,0.000002163696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00140201,"about_ca_system_score_gemma":0.00008528749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001458345,"about_ca_topic_score_gemma":8.944392e-7,"domain_scores_codex":[0.9971534,0.000226496,0.001556281,0.0000532526,0.0007284863,0.0002820893],"domain_scores_gemma":[0.9931409,0.0006140759,0.004591678,0.0002196412,0.001375682,0.00005801474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002986458,0.000008312869,0.002328265,0.00008700114,0.00007543219,1.002356e-8,0.005813526,0.3015355,0.00002125937,0.6874472,0.0004111773,0.002242501],"study_design_scores_gemma":[0.0007692049,0.00004187453,0.0009663487,0.0001152811,0.00003489517,0.000009143238,0.0009325986,0.9703026,0.0002106117,0.02057321,0.005898312,0.0001458728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002372711,0.00008462388,0.9942944,0.0001361034,0.001465463,0.001035334,0.0000209211,0.00004410109,0.0005463541],"genre_scores_gemma":[0.9846475,0.000004060951,0.01493188,0.00008013595,0.000173812,0.00008199487,0.00001386786,0.000005687847,0.00006106136],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9822748,"threshold_uncertainty_score":0.9905034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02218692379846194,"score_gpt":0.2928570431633438,"score_spread":0.2706701193648818,"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."}}