{"id":"W4247085996","doi":"10.32920/ryerson.14645826","title":"Privacy in mobile learning applications: user privacy concerns and implications of applying privacy by design approach","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Internet privacy; Privacy policy; Personally identifiable information; Information privacy; Privacy by Design; Privacy software; Computer science; Privacy protection; Mobile apps; Government (linguistics); Information sensitivity; Computer security; World Wide Web","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001841838,0.0003873767,0.0006540288,0.000269774,0.0006764374,0.0004250452,0.001647211,0.0006334035,0.0001141396],"category_scores_gemma":[0.001076891,0.000434219,0.0001296918,0.0007853968,0.0004499169,0.0005860885,0.003192342,0.001187355,0.000006672081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003409956,"about_ca_system_score_gemma":0.0008043681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005894247,"about_ca_topic_score_gemma":0.0001615725,"domain_scores_codex":[0.995967,0.000878459,0.0008548225,0.00122869,0.0005175899,0.0005534235],"domain_scores_gemma":[0.9970804,0.0004415968,0.0006235388,0.001338304,0.0002966209,0.0002195438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002355653,0.006548407,0.1731339,0.005440343,0.000874677,0.000007572807,0.2676142,0.006280263,0.02045554,0.09564371,0.01810242,0.4056634],"study_design_scores_gemma":[0.002875371,0.0002297822,0.01159774,0.000737138,0.0003467247,0.00001330478,0.05468595,0.004377584,0.004420708,0.05988507,0.8574299,0.003400799],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05181527,0.008117157,0.9174305,0.001027743,0.0001500104,0.0123802,0.00010862,0.0004388244,0.008531695],"genre_scores_gemma":[0.9216697,0.008458066,0.05646002,0.00007809137,0.0002796847,0.01186894,0.0006657569,0.00006071557,0.0004590529],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8698544,"threshold_uncertainty_score":0.9998109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06515553248655925,"score_gpt":0.3435192979968903,"score_spread":0.2783637655103311,"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."}}