{"id":"W3019770203","doi":"10.1108/jkm-02-2020-0081","title":"Big data for small and medium-sized enterprises (SME): a knowledge management model","year":2020,"lang":"en","type":"article","venue":"Journal of Knowledge Management","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Big data; Computer science; Data science; Knowledge management; Conceptual model; Small and medium-sized enterprises; Context (archaeology); Business intelligence; Exploit; Business; Data mining; Database","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007340046,0.0006346953,0.0005208453,0.004564551,0.003914788,0.01251867,0.002950992,0.004172471,0.004731029],"category_scores_gemma":[0.01276751,0.0005218763,0.001288787,0.005624883,0.007728751,0.01988018,0.00647452,0.00285949,0.001007539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007613312,"about_ca_system_score_gemma":0.01222302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007756527,"about_ca_topic_score_gemma":0.005926067,"domain_scores_codex":[0.9957004,0.001827567,0.0003285913,0.0005871618,0.001053047,0.0005032275],"domain_scores_gemma":[0.9842279,0.009649504,0.001243393,0.001014087,0.00190013,0.001964976],"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":[0.00003434128,0.0001605396,0.004807501,0.0002930335,0.00005021218,0.0005906034,0.004272365,0.0132356,0.0002621733,0.9530654,0.00407564,0.01915266],"study_design_scores_gemma":[0.00003799418,0.00007952975,0.002439216,0.0006652026,0.00006133369,0.0003851219,0.008531256,0.090207,0.0003837006,0.8556744,0.04147355,0.00006154005],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.123294,0.004699669,0.3910125,0.1254696,0.0003966425,0.001380615,0.001068102,0.0003516983,0.3523271],"genre_scores_gemma":[0.9159768,0.003033966,0.06547556,0.002216896,0.00019154,0.0007570015,0.0003976541,0.00004916964,0.01190133],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01251867,"threshold_uncertainty_score":0.05523872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2042581756848079,"score_gpt":0.3195073242493034,"score_spread":0.1152491485644954,"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."}}