{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005400437,0.0002095308,0.0003425615,0.001024458,0.001153623,0.002504274,0.0002684608,0.001052653,0.003090397],"category_scores_gemma":[0.01525028,0.0003085269,0.0005066379,0.000557899,0.0009943363,0.002492838,0.001551358,0.0008872933,0.0003922747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005821585,"about_ca_system_score_gemma":0.0007100106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004704073,"about_ca_topic_score_gemma":0.005428174,"domain_scores_codex":[0.9978253,0.001110708,0.0002312114,0.0001865406,0.0004046384,0.0002415871],"domain_scores_gemma":[0.9886568,0.006619037,0.002313661,0.0004986103,0.0008991864,0.001012734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001056352,0.0002944167,0.8170269,0.0001866391,0.00005870641,0.0003389157,0.1595724,0.00009670581,0.001288075,0.0003923075,0.0004465095,0.0201929],"study_design_scores_gemma":[0.00002327662,0.0005393451,0.7646887,0.0004084487,0.00007575634,0.0008657651,0.2248843,0.001117717,0.0004630114,0.0007497927,0.006108244,0.0000756355],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978974,0.0000944545,0.00029892,0.0003017722,0.000004004934,0.00001293799,0.00003469131,0.000003367363,0.001352391],"genre_scores_gemma":[0.9990193,0.0001254883,0.0003220082,0.0001352797,0.000003867682,0.00001937221,0.0000364758,0.000001570855,0.0003368238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005400437,"threshold_uncertainty_score":0.02856058,"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."}}