{"id":"W4206331798","doi":"10.53106/256299802019120101002","title":"Big Data: Ideology vs. Enlightenment","year":2019,"lang":"en","type":"article","venue":"International Journal of Computer Auditing","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Ideology; Big data; Politics; Epistemology; Philosophy; Political science; Computer science; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001528504,0.00008369163,0.0002154781,0.0003064557,0.00004080203,0.0002480451,0.004450898,0.00005261245,0.0002136995],"category_scores_gemma":[0.0004743756,0.00005950107,0.00008041153,0.0001867202,0.00005326038,0.000497796,0.001470029,0.0002212101,0.00042079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004617482,"about_ca_system_score_gemma":0.00006535059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005747948,"about_ca_topic_score_gemma":0.000003387601,"domain_scores_codex":[0.9975168,0.00004829175,0.0008278824,0.0002630881,0.001209356,0.000134605],"domain_scores_gemma":[0.9968421,0.0007483431,0.0009895517,0.0006453081,0.000726853,0.00004776945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002042132,0.00005598975,0.01156181,9.513026e-7,0.00009093399,0.00005251013,0.00004309112,0.0003818574,0.0004785495,0.007530312,0.07968594,0.9000977],"study_design_scores_gemma":[0.0006991572,0.0001411204,0.02147167,0.00006072137,0.00001086514,0.0007830483,0.0002228018,0.01796612,0.000744329,0.02889399,0.9288548,0.0001514307],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3774764,0.0001254811,0.5810943,0.03100618,0.007985234,0.000110214,0.0001035391,0.00003849168,0.002060145],"genre_scores_gemma":[0.9678116,0.00003673555,0.02905184,0.001086513,0.0018532,7.757074e-7,0.00001645296,0.000005446704,0.0001374219],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8999462,"threshold_uncertainty_score":0.827095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2478820722368575,"score_gpt":0.3951213362030417,"score_spread":0.1472392639661842,"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."}}