{"id":"W4242436817","doi":"10.32920/ryerson.14651715","title":"Comprehending privacy in hindsight","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Freedom of Expression and Defamation","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; York University","funders":"","keywords":"Scope (computer science); Premise; Hindsight bias; Information privacy; Context (archaeology); Internet privacy; Right to privacy; Privacy policy; Law and economics; Political science; Perspective (graphical); Business; Sociology; Computer science; Psychology; Epistemology; History; Social psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01385013,0.0009363979,0.0005665419,0.001450934,0.005732029,0.012621,0.002330009,0.005908052,0.006972662],"category_scores_gemma":[0.01759858,0.0004584769,0.000679897,0.001216597,0.04254764,0.02513332,0.008452534,0.007213553,0.001226868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004562533,"about_ca_system_score_gemma":0.005016031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005977063,"about_ca_topic_score_gemma":0.002989925,"domain_scores_codex":[0.9901658,0.005966203,0.0004011383,0.001295919,0.001524986,0.0006460383],"domain_scores_gemma":[0.9889787,0.007273493,0.0008168062,0.001739786,0.0009563275,0.0002349324],"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":[0.000002529961,7.937452e-7,0.00004160683,0.00001415583,0.000001422135,0.0000261583,0.002449358,0.00007606732,0.00002056241,0.9934998,0.001690969,0.002176637],"study_design_scores_gemma":[0.000004549273,0.000005578353,0.00006135168,0.000130256,0.000003638818,0.000119272,0.002021468,0.000382202,0.0001248421,0.901208,0.09593249,0.000006289317],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01615036,0.01219894,0.3282529,0.1266224,0.003040827,0.0001061693,0.0002629097,0.0003282184,0.5130372],"genre_scores_gemma":[0.8643829,0.008029281,0.03709981,0.03182185,0.003315095,0.0003034458,0.0002930674,0.0003251406,0.0544294],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01385013,"threshold_uncertainty_score":0.07324743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06646712794019143,"score_gpt":0.3682730607276415,"score_spread":0.30180593278745,"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."}}