{"id":"W3215899368","doi":"10.3390/digital1040015","title":"Improving Readability of Online Privacy Policies through DOOP: A Domain Ontology for Online Privacy","year":2021,"lang":"en","type":"article","venue":"Digital","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Readability; Jargon; Privacy policy; Computer science; Internet privacy; Ontology; Domain (mathematical analysis); Privacy software; Key (lock); Information privacy; World Wide Web; Action (physics); Reading (process); Computer security; Political science; Law","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005034852,0.0009085905,0.0006385832,0.002743468,0.001321201,0.003438963,0.001537793,0.001180901,0.003736553],"category_scores_gemma":[0.01768193,0.0006357112,0.001172474,0.001692549,0.002078201,0.01002278,0.004085467,0.003204044,0.001134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001852563,"about_ca_system_score_gemma":0.003530296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006512893,"about_ca_topic_score_gemma":0.00723176,"domain_scores_codex":[0.9958014,0.001181039,0.0006391183,0.0008307326,0.001333549,0.0002140823],"domain_scores_gemma":[0.9827073,0.008612207,0.001586234,0.0039333,0.002620771,0.0005402521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004501114,0.001122874,0.01222959,0.002477821,0.0001050826,0.00154343,0.01973109,0.00893593,0.08505734,0.1709073,0.02883249,0.6686069],"study_design_scores_gemma":[0.0001252002,0.0004041203,0.0111019,0.0009185612,0.0002343736,0.003069155,0.01061567,0.1562466,0.1269072,0.1237883,0.5661873,0.0004016287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03503537,0.0002310581,0.943397,0.00192813,0.0001417785,0.001065881,0.002245529,0.007577487,0.008377773],"genre_scores_gemma":[0.1298285,0.0004264224,0.8589177,0.0004727967,0.00004118294,0.0005316539,0.003814195,0.0009328322,0.005034844],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006512893,"threshold_uncertainty_score":0.02662712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04368870550553924,"score_gpt":0.3446450082676642,"score_spread":0.300956302762125,"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."}}