{"id":"W3147668544","doi":"","title":"Big Data, Information Technology and Information Professionals: Some Considerations for Digital Ethics.","year":2020,"lang":"en","type":"article","venue":"Medical Informatics Europe","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Ambiguity; Information ethics; Ethical issues; Engineering ethics; Big data; Information technology; Data science; Computer science; Knowledge management; Management science; Internet privacy; Sociology; Engineering","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":["metaresearch","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005885621,0.0001608222,0.0001885831,0.0003461003,0.0003082681,0.0009164276,0.0005131735,0.0002222232,0.0001127953],"category_scores_gemma":[0.01262504,0.000129133,0.00001919106,0.0006605762,0.0002015354,0.02671496,0.0008292838,0.0004646616,0.000851294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006436316,"about_ca_system_score_gemma":0.0001760484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005651467,"about_ca_topic_score_gemma":0.000002620589,"domain_scores_codex":[0.9982775,0.000005675788,0.0008262557,0.0000957513,0.0005678395,0.0002269641],"domain_scores_gemma":[0.9984642,0.0002345061,0.0003675866,0.0003034938,0.0005756792,0.00005458239],"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":[0.00005188067,0.00006222624,0.0004917465,0.002590305,0.00004272441,0.000002342801,0.001866392,0.0000250254,0.000003825877,0.5326586,0.191421,0.2707839],"study_design_scores_gemma":[0.000425886,0.00001592595,0.00006461857,0.000101944,0.00002056224,0.000009005465,0.0007491787,0.08796402,0.00001304957,0.004515788,0.9059331,0.0001868599],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01704166,0.0001354525,0.6302605,0.3218299,0.002707953,0.002335828,0.001070409,0.001151497,0.02346689],"genre_scores_gemma":[0.6215654,0.0003132848,0.00799744,0.3556008,0.003290896,0.0001283006,0.01099529,0.00004493372,0.00006360741],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.7145122,"threshold_uncertainty_score":0.9999267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1613664367123291,"score_gpt":0.3256388331361766,"score_spread":0.1642723964238475,"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."}}