{"id":"W2594845050","doi":"","title":"Personalization, analytics, and sponsored services: The challenges of applying PIPEDA to online tracking and profiling activities","year":2010,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Public Works and Government Services Canada; Social Sciences and Humanities Research Council of Canada; Government of Canada","keywords":"Profiling (computer programming); Personalization; Analytics; Computer science; Tracking (education); World Wide Web; Data science; Internet privacy; Psychology; Operating system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01458879,0.0009040611,0.001345018,0.005049363,0.00256504,0.01714281,0.003488211,0.004519996,0.004466645],"category_scores_gemma":[0.03763434,0.0008915888,0.0008892993,0.009083903,0.006468611,0.02882739,0.009559977,0.007568583,0.003049928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003761202,"about_ca_system_score_gemma":0.003634348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01204164,"about_ca_topic_score_gemma":0.008166832,"domain_scores_codex":[0.987332,0.006081824,0.0005955742,0.001723736,0.003668763,0.0005980238],"domain_scores_gemma":[0.9583195,0.02466009,0.001903064,0.007762805,0.005479492,0.001875072],"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":[0.0001398468,0.0001965536,0.01306283,0.0006627044,0.00007827344,0.000356091,0.003378386,0.006342822,0.0007520255,0.3043801,0.03687293,0.6337774],"study_design_scores_gemma":[0.00002556086,0.00009249018,0.005469577,0.001023283,0.00003910928,0.0006758004,0.0058735,0.09360435,0.001175219,0.552012,0.3398854,0.0001238064],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04439038,0.08946612,0.4843622,0.2675467,0.002144441,0.000596575,0.001781423,0.003678255,0.106034],"genre_scores_gemma":[0.5725103,0.0654012,0.3196498,0.01320475,0.003909668,0.0006116829,0.001772834,0.0008802298,0.02205945],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01714281,"threshold_uncertainty_score":0.0771538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04688490487987235,"score_gpt":0.279357892622574,"score_spread":0.2324729877427016,"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."}}