{"id":"W2405781509","doi":"","title":"Rich Data: Risks, Issues, Controversies & Hype.","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Big data; Government (linguistics); Promotion (chess); Business; State (computer science); Analytics; Marketing; Data science; Political science; Computer science; Politics; Law","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":[0.08079533,0.0008554437,0.001273057,0.004776047,0.006582079,0.02063781,0.003515619,0.01533485,0.007083941],"category_scores_gemma":[0.1419949,0.0008578149,0.001145163,0.005808735,0.04035801,0.04240358,0.01130661,0.02196051,0.002105163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005124167,"about_ca_system_score_gemma":0.008308145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003507378,"about_ca_topic_score_gemma":0.005616685,"domain_scores_codex":[0.9560171,0.02581817,0.002104419,0.002306305,0.01264448,0.001109678],"domain_scores_gemma":[0.7350611,0.2308502,0.005413401,0.008413149,0.01550871,0.004753525],"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":[0.00009284661,0.00005439391,0.001575258,0.001485166,0.0000780069,0.0003838485,0.003166861,0.000335694,0.0001773078,0.5793401,0.2317779,0.1815327],"study_design_scores_gemma":[0.00003879279,0.00006477319,0.001074947,0.006309731,0.00004141078,0.0007338228,0.01009202,0.001007321,0.0003179185,0.4924486,0.4877695,0.0001011721],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0005374209,0.08186176,0.002434817,0.9059332,0.003389028,0.00001556047,0.00004367066,0.00002268103,0.005761911],"genre_scores_gemma":[0.1089903,0.3499964,0.01507098,0.4723581,0.04551716,0.0003250387,0.0002047583,0.0001859891,0.007351269],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08079533,"threshold_uncertainty_score":0.4272917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.803639955861975,"score_gpt":0.5643870837347821,"score_spread":0.2392528721271929,"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."}}