{"id":"W2946041163","doi":"10.1016/j.techfore.2019.05.005","title":"Digital technology, digital culture and the metric/nonmetric distinction","year":2019,"lang":"en","type":"article","venue":"Technological Forecasting and Social Change","topic":"University-Industry-Government Innovation Models","field":"Business, Management and Accounting","cited_by":69,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Obstacle; Metric (unit); Aside; Phenomenon; Digital transformation; Epistemology; Sociology; Transformation (genetics); Computer science; Mathematics; Philosophy; Linguistics; Political science; Economics; Law; Operations management; World Wide Web","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":[0.0002084383,0.0001675893,0.0002135874,0.0003921969,0.0004507661,0.0004691941,0.0001948376,0.0004795703,0.00002584452],"category_scores_gemma":[0.0003793358,0.0001127994,0.00004555387,0.001969886,0.000468543,0.001055727,0.0005473418,0.0005321511,0.0000305041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004446852,"about_ca_system_score_gemma":0.000002425853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000137252,"about_ca_topic_score_gemma":0.000001177907,"domain_scores_codex":[0.9990762,0.000003720132,0.0001542319,0.0002864006,0.0002367455,0.0002426524],"domain_scores_gemma":[0.9995711,0.00005691482,0.000185222,0.0001017237,0.00007770407,0.000007291903],"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":[0.00007802562,0.00007639926,0.1427079,0.000071079,0.00004454523,0.000008817262,0.000156972,7.921864e-7,0.00001779008,0.4734983,0.000303171,0.3830363],"study_design_scores_gemma":[0.01433263,0.0004210213,0.04875316,0.0002507338,0.0004266353,0.0001244353,0.03483405,0.03647648,0.0000615844,0.240473,0.6208127,0.003033495],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9670799,0.0000940797,0.0004293617,0.004076506,0.00008321219,0.0004347086,0.00001668713,0.0005402215,0.02724537],"genre_scores_gemma":[0.9983624,0.000006189167,0.00003651782,0.0003141175,0.0002762918,0.00001498962,0.00002265474,0.00001125928,0.0009555685],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6205096,"threshold_uncertainty_score":0.4599825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04523808100410318,"score_gpt":0.2143044679508202,"score_spread":0.169066386946717,"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."}}