{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000383107,0.0001666809,0.0001991901,0.0001080333,0.0002632651,0.0003222669,0.0002576494,0.00007337618,0.00006505239],"category_scores_gemma":[0.0001174795,0.0001261508,0.00002753878,0.0002543153,0.0001389285,0.001088756,0.0002073741,0.0002045723,0.00000854636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005058196,"about_ca_system_score_gemma":0.00001374975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009847848,"about_ca_topic_score_gemma":0.006436056,"domain_scores_codex":[0.9990578,0.00001083918,0.0002253461,0.0002933732,0.0002121489,0.0002005505],"domain_scores_gemma":[0.9992961,0.00008463603,0.0001534914,0.0002645597,0.0001763001,0.00002492818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002665897,0.0003136226,0.1419095,0.005735798,0.0001687278,0.00001145529,0.001823449,0.000182638,0.05475559,0.717265,0.0001993368,0.07736836],"study_design_scores_gemma":[0.002872864,0.0001069582,0.3964904,0.003430594,0.001012323,0.00009142622,0.07031178,0.05614926,0.02800944,0.02969643,0.4081764,0.003652107],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954264,0.0008963887,0.0005014993,0.001449429,0.0002208443,0.0003421606,0.0000225624,0.0000582344,0.001082536],"genre_scores_gemma":[0.997429,0.0001523759,0.0007329317,0.0008976602,0.0006609658,0.00001879291,0.00003008868,0.00002132299,0.00005688046],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6875685,"threshold_uncertainty_score":0.5144281,"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."}}