{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09715975,0.0008979748,0.00106236,0.002693082,0.005817764,0.01574916,0.002811728,0.005702064,0.001454288],"category_scores_gemma":[0.1284816,0.001030421,0.001506854,0.002510004,0.01645791,0.01833404,0.006865104,0.00772188,0.000421912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005779071,"about_ca_system_score_gemma":0.009171954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002005409,"about_ca_topic_score_gemma":0.001654641,"domain_scores_codex":[0.8321183,0.127586,0.004995097,0.005240522,0.02680621,0.003253895],"domain_scores_gemma":[0.7095285,0.2338438,0.01058099,0.02597519,0.01806335,0.002008184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001607096,0.0003504386,0.02057361,0.001526407,0.0001786446,0.001053576,0.04879244,0.004536666,0.003278736,0.7644696,0.004513948,0.1505653],"study_design_scores_gemma":[0.0001080674,0.0009150452,0.008888147,0.004180071,0.0003398095,0.004893321,0.04552852,0.02487271,0.01263553,0.7195131,0.1778921,0.0002335817],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1008096,0.01073357,0.7084789,0.1078981,0.0006022632,0.0009372934,0.00009447424,0.0003918987,0.07005385],"genre_scores_gemma":[0.8382148,0.004937263,0.1450413,0.006251021,0.0003271051,0.0007144018,0.00006202656,0.000128582,0.004323589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09715975,"threshold_uncertainty_score":0.513836,"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."}}