{"id":"W3154165027","doi":"10.32920/22227871","title":"Privacy by Design by Regulation: The Case Study of Ontario","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Tel Aviv University","keywords":"Commission; Regulator; European commission; Privacy by Design; Business; Information privacy; European union; General Data Protection Regulation; Privacy policy; Lottery; Internet privacy; FTC Fair Information Practice; Public relations; Data Protection Act 1998; Political science; Public administration; Information privacy law; Law; Economics; Computer science; Finance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004968024,0.000414405,0.000451771,0.001102481,0.04447946,0.005448926,0.002376299,0.004524004,0.003412022],"category_scores_gemma":[0.01488276,0.0005396041,0.0005218496,0.004252141,0.01912598,0.002353057,0.004313766,0.003457972,0.0002930008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1016222,"about_ca_system_score_gemma":0.0760911,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9441922,"about_ca_topic_score_gemma":0.9770486,"domain_scores_codex":[0.9872473,0.00553603,0.0002917032,0.0009109474,0.002910508,0.003103471],"domain_scores_gemma":[0.9884126,0.005540844,0.001597403,0.0008680925,0.001943094,0.001637992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001190746,0.0002434125,0.03270664,0.000202586,0.00003374621,0.0139444,0.8622907,0.0009079106,0.001155736,0.05926815,0.01320769,0.01591991],"study_design_scores_gemma":[0.0000463211,0.0001265465,0.03014977,0.0002180057,0.00003977921,0.001555962,0.7723591,0.001224141,0.001049617,0.002982381,0.1901637,0.00008462544],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8924465,0.0007105394,0.002328351,0.01517909,0.00005925028,0.0002810934,0.0001042472,0.00002508575,0.08886579],"genre_scores_gemma":[0.9808807,0.0007288256,0.0008888632,0.001397102,0.00001408899,0.0001062067,0.00004083011,0.00001861571,0.01592476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1016222,"threshold_uncertainty_score":0.7373245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09229694213529444,"score_gpt":0.2974246749665958,"score_spread":0.2051277328313014,"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."}}