{"id":"W2587304703","doi":"","title":"The Evolution of Consumer Privacy Law: How Privacy by Design Can Benefit from Insights in Commercial Law and Standardization","year":2012,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Legal Systems and Judicial Processes","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Scope (computer science); Personally identifiable information; Privacy by Design; Business; Consumer protection; Temptation; Standardization; Transferability; Information privacy; Information privacy law; Limiting; Internet privacy; Privacy policy; FTC Fair Information Practice; Law and economics; Law; Computer science; Economics; Political science; Incentive; Microeconomics; Engineering","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":[0.0422323,0.0007145426,0.0009230619,0.002446553,0.00584747,0.01558282,0.002636805,0.01005431,0.003597951],"category_scores_gemma":[0.03598465,0.000947313,0.001154837,0.001812584,0.07835108,0.02728188,0.006097071,0.01469548,0.0007242275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01004151,"about_ca_system_score_gemma":0.007608915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004856818,"about_ca_topic_score_gemma":0.003445428,"domain_scores_codex":[0.9703283,0.01989326,0.0008417909,0.00252719,0.005141916,0.001267557],"domain_scores_gemma":[0.9637107,0.02442147,0.001353434,0.006970458,0.002799066,0.0007449286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000002895565,0.000006673823,0.00008274356,0.00001139245,0.000002726942,0.00001887338,0.001198736,0.0002008249,0.00004320138,0.9951507,0.0004836032,0.002797635],"study_design_scores_gemma":[0.000008614517,0.00001787126,0.0001120332,0.00006992798,0.000004862893,0.00005366751,0.0005929515,0.0008327898,0.0001668606,0.9715636,0.02656276,0.00001419062],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0294073,0.0102847,0.3742414,0.2634641,0.001013894,0.0001989256,0.00008549445,0.0002519106,0.3210521],"genre_scores_gemma":[0.8484541,0.005673929,0.1031504,0.0192861,0.0009390871,0.0004388101,0.00007339124,0.0003323108,0.02165178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0422323,"threshold_uncertainty_score":0.2233484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01959248869254737,"score_gpt":0.2636593672511173,"score_spread":0.24406687855857,"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."}}