{"id":"W2293557490","doi":"10.1109/icmla.2015.143","title":"A Machine-Learning Based Approach for Measuring the Completeness of Online Privacy Policies","year":2015,"lang":"en","type":"article","venue":"","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Privacy policy; Completeness (order theory); Computer science; Information privacy; Transparency (behavior); Privacy software; Privacy by Design; Internet privacy; Computer security; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001658166,0.00007828178,0.0001404478,0.00005635332,0.0004676197,0.00005787916,0.0005768766,0.00005160302,0.00001159277],"category_scores_gemma":[0.002285971,0.00005366252,0.00006089699,0.0002321986,0.0001698049,0.0001603238,0.0001589131,0.0001169593,0.000001282038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005243715,"about_ca_system_score_gemma":0.000197349,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01746204,"about_ca_topic_score_gemma":0.001478275,"domain_scores_codex":[0.9988598,0.0002661955,0.0001721966,0.0001423887,0.0003422618,0.000217138],"domain_scores_gemma":[0.9992182,0.000161956,0.0001045966,0.0002345813,0.000200875,0.00007981434],"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":[0.001792766,0.00460616,0.1249259,0.001345389,0.0004271692,0.000002362063,0.2829203,0.02513767,0.005395459,0.4712145,0.01785247,0.06437983],"study_design_scores_gemma":[0.003597814,0.0004107196,0.00561713,0.0000556724,0.00009669724,0.00000284109,0.05589901,0.3741401,0.002812197,0.022051,0.534631,0.000685811],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1013317,0.0006666068,0.8471863,0.01308934,0.0003286013,0.002463573,0.000169954,0.0004591655,0.03430471],"genre_scores_gemma":[0.9877893,0.00001103237,0.01153294,0.0001591787,0.0002191653,0.00003420341,0.00005891621,0.000008762137,0.0001865239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8864576,"threshold_uncertainty_score":0.9890808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1441032366687973,"score_gpt":0.3373883054219519,"score_spread":0.1932850687531546,"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."}}