{"id":"W1503976677","doi":"10.17705/1jais.00276","title":"Fitting Covariance Models for Theory Generation","year":2011,"lang":"en","type":"article","venue":"Journal of the Association for Information Systems","topic":"Technology Adoption and User Behaviour","field":"Decision Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Memorial University of Newfoundland","funders":"","keywords":"Covariance; Structural equation modeling; Computer science; Phenomenon; Econometrics; Data mining; Algorithm; Mathematics; Epistemology; Machine learning; Statistics; Philosophy","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":[],"consensus_categories":[],"category_scores_codex":[0.01195546,0.00007965016,0.0002210865,0.0002593571,0.0003018419,0.0002384615,0.0005941005,0.0001749325,0.000006387631],"category_scores_gemma":[0.005122137,0.00004948661,0.0002594853,0.0002996528,0.00001444931,0.002462368,0.00003050585,0.0001269978,0.00001908389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002265435,"about_ca_system_score_gemma":0.00009569191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002741193,"about_ca_topic_score_gemma":0.000001596457,"domain_scores_codex":[0.9973555,0.0001857989,0.001441873,0.00006676391,0.0008092455,0.0001408558],"domain_scores_gemma":[0.9920354,0.0006136321,0.004389485,0.0002280473,0.002697882,0.00003553314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000244484,0.0000731263,0.02099893,0.00002573358,0.0001712935,1.132466e-7,0.01097701,0.02549714,0.0002647433,0.848357,0.08127385,0.01211655],"study_design_scores_gemma":[0.007151213,0.0005018556,0.04825567,0.0001625996,0.000330326,0.0001030875,0.01710678,0.2779739,0.003099714,0.2826689,0.3620096,0.0006363036],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04616854,0.00005417603,0.9441931,0.0007050067,0.005176841,0.0008343404,0.00008513108,0.00003472103,0.00274819],"genre_scores_gemma":[0.9938469,0.000003219867,0.003320697,0.000240755,0.000207679,0.00003617419,0.000003908195,0.000005289482,0.002335364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9476784,"threshold_uncertainty_score":0.6132046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1963874657033897,"score_gpt":0.3450441081003986,"score_spread":0.1486566423970088,"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."}}