{"id":"W3125614544","doi":"10.2139/ssrn.3252869","title":"Information: Hard and Soft","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":191,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Intermediary; Incentive; Process (computing); Computer science; Component (thermodynamics); Financial market; Business; Financial intermediary; Information structure; Data science; Knowledge management; Marketing; Finance; Microeconomics; Economics","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":[0.0008984608,0.00007918565,0.0001912909,0.0001565155,0.0002383333,0.0001475415,0.0001174604,0.00003765854,0.0004955238],"category_scores_gemma":[0.00003616721,0.00008014997,0.0000736248,0.0001536342,0.00004723088,0.0005131126,0.00003149603,0.0003287955,0.0007576217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001756754,"about_ca_system_score_gemma":0.0001066065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002290828,"about_ca_topic_score_gemma":0.0002977895,"domain_scores_codex":[0.998719,0.000006553542,0.0003902872,0.000086452,0.00002948468,0.0007682226],"domain_scores_gemma":[0.9995161,0.000007837014,0.0002444933,0.000124971,0.00005130684,0.00005527051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000009465951,0.000006134559,0.008211805,0.00000330774,0.0001230516,1.620866e-7,0.0002809875,0.000001812547,0.000001680754,0.9795207,0.0004793971,0.01136145],"study_design_scores_gemma":[0.0004002523,0.0002337266,0.005652791,0.000005032904,0.000009575036,0.0002128183,0.001096769,0.000930109,0.000002814076,0.5451393,0.4461476,0.0001693259],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6591169,0.02477131,0.204403,0.005847965,0.001260308,0.0002705926,0.00005015596,0.00009758916,0.1041823],"genre_scores_gemma":[0.9950219,0.000994799,0.0001018916,0.0001530547,0.000398726,0.000001625195,0.000002285298,0.000006790101,0.003318956],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4456682,"threshold_uncertainty_score":0.9737947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01123238797064553,"score_gpt":0.1894446246985302,"score_spread":0.1782122367278847,"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."}}