{"id":"W2185585739","doi":"10.29173/irie365","title":"The Ethics of Big Data in Higher Education","year":2014,"lang":"en","type":"article","venue":"The International Review of Information Ethics","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Normative; Big data; Data science; Nexus (standard); Value (mathematics); Engineering ethics; Psychological intervention; Computer science; Knowledge management; Political science; Psychology; Data mining; Engineering; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008217273,0.00005667153,0.0001034387,0.00006189728,0.00007513499,0.00005579872,0.002494022,0.00006576067,0.000007709531],"category_scores_gemma":[0.006983113,0.00003236073,0.00003493121,0.0002646389,0.00008361536,0.0005527431,0.0003727891,0.000720742,0.00002159181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002195415,"about_ca_system_score_gemma":0.0004996976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004879125,"about_ca_topic_score_gemma":0.00001625292,"domain_scores_codex":[0.9982899,0.0002531556,0.0005930736,0.00006523602,0.0007328605,0.00006579477],"domain_scores_gemma":[0.995909,0.001982002,0.0005677248,0.0007848696,0.0007410263,0.00001533939],"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":[0.000001221243,0.00001442288,0.00007201808,0.0005384235,0.000008335634,8.689848e-9,0.0005188033,0.0000995579,0.000001845187,0.857721,0.001567264,0.1394571],"study_design_scores_gemma":[0.00008512814,0.00001711167,0.001756603,0.002895494,0.000008613677,0.000002371742,0.00006560246,0.04882637,0.00002537375,0.0319962,0.9142569,0.00006426503],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0001728912,0.003949428,0.07447439,0.8923438,0.002430535,0.0002008647,0.00001337209,0.00002540842,0.02638938],"genre_scores_gemma":[0.8037846,0.1067788,0.01658085,0.06967431,0.0007141614,0.00002256252,0.0003425089,0.00001278457,0.002089469],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.9126896,"threshold_uncertainty_score":0.8359943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1262834686519953,"score_gpt":0.4037729858606227,"score_spread":0.2774895172086274,"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."}}