{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005151851,0.0002094103,0.0002695557,0.00004028899,0.0002367527,0.0001281944,0.001113563,0.00006790829,0.00007134506],"category_scores_gemma":[0.00001527745,0.0001371178,0.0000716998,0.0001573874,0.00003695624,0.0001018701,0.003190933,0.0002531391,0.00002018002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001417371,"about_ca_system_score_gemma":0.0001842579,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3697466,"about_ca_topic_score_gemma":0.06473766,"domain_scores_codex":[0.9984794,0.0001149565,0.000403177,0.0004868433,0.0003343186,0.0001812657],"domain_scores_gemma":[0.9982302,0.0001919823,0.0001593321,0.001296596,0.00008830973,0.00003362774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001042747,0.0005762205,0.001542283,0.00003906959,0.000573881,0.0002013887,0.0778463,0.002874068,0.00003396048,0.004609567,0.9066756,0.005017255],"study_design_scores_gemma":[0.01541371,0.009438851,0.04424624,0.0008207106,0.002446463,0.001148249,0.06218757,0.129525,0.01488707,0.08060075,0.6286713,0.0106141],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2297397,0.0002223648,0.7574008,0.001569984,0.0009401992,0.002730865,0.000007435185,0.0003887015,0.007000019],"genre_scores_gemma":[0.9427655,0.00001178615,0.002931094,0.0001165173,0.00003121711,0.0001421564,0.000009265387,0.00001315057,0.05397936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7544697,"threshold_uncertainty_score":0.9523284,"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."}}