{"id":"W3072329718","doi":"10.3390/info11080399","title":"Preventative Nudges: Introducing Risk Cues for Supporting Online Self-Disclosure Decisions","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; Universität Duisburg-Essen; Deutsche Forschungsgemeinschaft; European Commission","keywords":"Nudge theory; Internet privacy; SAFER; Risk perception; Perception; Private information retrieval; Computer science; Work (physics); Computer security; Psychology; Social psychology","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":[0.007656236,0.001201882,0.0004862019,0.0009848938,0.001574785,0.003445051,0.001679287,0.002333883,0.005696408],"category_scores_gemma":[0.05199283,0.0005668865,0.0005971771,0.0003439596,0.001765668,0.005479363,0.004980891,0.002439351,0.0007554927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006097322,"about_ca_system_score_gemma":0.001447652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005523673,"about_ca_topic_score_gemma":0.001017981,"domain_scores_codex":[0.9933811,0.004546761,0.0003732809,0.0006145734,0.0008199314,0.0002643176],"domain_scores_gemma":[0.9562079,0.02862709,0.005534808,0.006075209,0.00159673,0.001958195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002662668,0.005905036,0.05377343,0.00204772,0.0003871389,0.002708684,0.05465263,0.03838576,0.05079936,0.1647243,0.01212853,0.6118248],"study_design_scores_gemma":[0.0007919695,0.005760956,0.02941826,0.002829565,0.001022629,0.002929364,0.01990204,0.3793595,0.06729553,0.2978141,0.1919004,0.0009756633],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3438977,0.0007754616,0.6091439,0.005356934,0.0003728883,0.001333383,0.0002324016,0.005838478,0.03304887],"genre_scores_gemma":[0.8256966,0.0002032138,0.1705647,0.0004389925,0.0000534933,0.0004303403,0.00008628528,0.0000880158,0.002438323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007656236,"threshold_uncertainty_score":0.04049051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0357087967049944,"score_gpt":0.3462563621759767,"score_spread":0.3105475654709823,"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."}}