{"id":"W3032023495","doi":"10.1145/3313831.3376651","title":"Understanding Fitness Tracker Users' Security and Privacy Knowledge, Attitudes and Behaviours","year":2020,"lang":"en","type":"article","venue":"","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; York University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Activity tracker; Internet privacy; BitTorrent tracker; Computer science; Tracking (education); Work (physics); Information privacy; Psychology; Eye tracking; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003901858,0.0001101155,0.0001399314,0.00003985613,0.0006589799,0.0002338733,0.0002053514,0.0001139722,0.0001349174],"category_scores_gemma":[0.0003537067,0.000107089,0.00002339311,0.0002065369,0.0003327175,0.0006444484,0.0002845296,0.0001660321,0.00000900685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007405108,"about_ca_system_score_gemma":0.00006742208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001423627,"about_ca_topic_score_gemma":0.00537719,"domain_scores_codex":[0.999016,0.000131161,0.0001266787,0.0003126201,0.0001768597,0.0002367128],"domain_scores_gemma":[0.9994589,0.00008586988,0.00003955274,0.0001231613,0.00003559437,0.0002568916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001139564,0.0003910219,0.3278751,0.0003924102,0.00007285004,0.00002866846,0.3025453,4.845986e-7,0.0002518279,0.3424915,0.01920522,0.006631649],"study_design_scores_gemma":[0.007715236,0.001076957,0.4025455,0.0003821028,0.000500682,0.00004423736,0.1852404,0.003052864,0.002438901,0.2375927,0.1553891,0.004021283],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9669429,0.001467224,0.004967907,0.01096748,0.0002562279,0.0005921441,0.00003213732,0.0003176113,0.01445634],"genre_scores_gemma":[0.9986868,0.0006124593,0.0002516717,0.0001848114,0.0001861964,0.000006245734,0.000006589307,0.0000084221,0.00005681779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1361839,"threshold_uncertainty_score":0.5068407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1258834345569466,"score_gpt":0.3465804254071519,"score_spread":0.2206969908502053,"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."}}