{"id":"W2560163360","doi":"","title":"FEARING ONLINE IDENTITY THEFT: A SEGMENTATION STUDY OF ONLINE CUSTOMERS","year":2016,"lang":"en","type":"article","venue":"European Conference on Information Systems","topic":"Digital Communication and Language","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Identity theft; Internet privacy; Computer science; Identity (music); Segmentation; Computer security; Business; Artificial intelligence; Aesthetics","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":[0.002577363,0.0002458791,0.0004874499,0.001321475,0.00547162,0.005603911,0.001317795,0.002333276,0.008059993],"category_scores_gemma":[0.01575794,0.0005304377,0.0003302704,0.001839253,0.002703985,0.007248726,0.002910914,0.003232117,0.001212826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002222318,"about_ca_system_score_gemma":0.00214309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02493465,"about_ca_topic_score_gemma":0.02817276,"domain_scores_codex":[0.9983819,0.0006553291,0.00005761715,0.0001570263,0.0002849133,0.0004633168],"domain_scores_gemma":[0.9881506,0.005412834,0.002673236,0.0005600186,0.001118448,0.002084827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0007591087,0.003466866,0.3918801,0.0000934227,0.00004867327,0.0006290175,0.5724787,0.0001209821,0.001723506,0.007287573,0.002154681,0.01935746],"study_design_scores_gemma":[0.00002586006,0.0003999306,0.3138863,0.00005751122,0.00002423682,0.0003899935,0.6786128,0.0009021995,0.0003841949,0.001538758,0.003725752,0.00005245557],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970606,0.00003541099,0.00009869529,0.0002983732,0.00000461586,0.00001790316,0.00002063024,0.000002362407,0.002461318],"genre_scores_gemma":[0.9986537,0.00003985863,0.00005507415,0.000235014,0.000005837593,0.00001643487,0.00003823233,0.000007473868,0.0009483507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02493465,"threshold_uncertainty_score":0.04957902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0510747633396778,"score_gpt":0.2966486221212811,"score_spread":0.2455738587816033,"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."}}