{"id":"W2036930014","doi":"10.1109/mts.2010.935989","title":"PIPWatch Toolbar: Using Social Navigation to Enhance Privacy Protection and Compliance","year":2010,"lang":"en","type":"article","venue":"IEEE Technology and Society Magazine","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"H2020 European Research Council","keywords":"Internet privacy; Legislation; The Internet; Compliance (psychology); Information privacy; Privacy policy; Personally identifiable information; Privacy protection; Privacy law; World Wide Web; Business; Computer security; Computer science; Political science","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005754508,0.0001316941,0.0001692387,0.00007312858,0.001524202,0.00008226578,0.0002421314,0.0005103951,0.0000141541],"category_scores_gemma":[0.0002137082,0.0001452345,0.0000394321,0.0006440287,0.0007380535,0.0003799006,0.0001678814,0.0006062654,0.00002034026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000594007,"about_ca_system_score_gemma":0.00006061557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002878851,"about_ca_topic_score_gemma":0.0002901076,"domain_scores_codex":[0.9989216,0.00004821929,0.0001701973,0.0003782004,0.0001687601,0.0003129754],"domain_scores_gemma":[0.9994788,0.00002084512,0.0000944077,0.0002066147,0.0001250374,0.00007435346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004510279,0.0001171582,0.001970025,0.0001181339,0.00003776489,0.000002758025,0.01763722,0.000001111814,0.8488011,0.01499094,0.00246795,0.1138107],"study_design_scores_gemma":[0.002708628,0.0007484205,0.03284652,0.0003699012,0.000191589,0.0001695333,0.01183416,0.005388943,0.1673611,0.3379864,0.437694,0.002700882],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.970265,0.00006686639,0.0168186,0.01116434,0.0004439541,0.0006194048,0.00001098665,0.0003317976,0.0002790513],"genre_scores_gemma":[0.9910921,0.00009533471,0.008007635,0.0001852866,0.0003816405,0.00005880943,0.000004186419,0.00001095855,0.0001640792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6814401,"threshold_uncertainty_score":0.9997756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0346985295857753,"score_gpt":0.3379854382288316,"score_spread":0.3032869086430563,"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."}}