{"id":"W4416410353","doi":"10.1016/j.tele.2025.102342","title":"How private is private enough? Evaluating facial de-identification across changing social contexts","year":2025,"lang":"en","type":"article","venue":"Telematics and Informatics","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Information Technology Research Centre; Ministry of Science, ICT and Future Planning","keywords":"Personalization; Process (computing); Generative grammar; Control (management); Empirical research; Face (sociological concept); Generative model","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002968097,0.0002586668,0.0003200287,0.0004780267,0.0006762858,0.001900369,0.0003325875,0.0009137762,0.001579967],"category_scores_gemma":[0.01718545,0.0001240182,0.0002853429,0.0003269167,0.0006097778,0.001461948,0.001051794,0.0007003002,0.0004626219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006485642,"about_ca_system_score_gemma":0.0003868597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005278299,"about_ca_topic_score_gemma":0.007954658,"domain_scores_codex":[0.9980337,0.0009219255,0.0001106616,0.0003075711,0.0004233426,0.0002027997],"domain_scores_gemma":[0.993123,0.003021308,0.001722771,0.0004789731,0.001277312,0.0003765772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002168088,0.000560771,0.8684942,0.0001310135,0.0002376534,0.0002004034,0.006164585,0.002675059,0.009893138,0.001027281,0.001153314,0.1072946],"study_design_scores_gemma":[0.00002278931,0.0007485061,0.9601121,0.00007695786,0.0002004248,0.0004066015,0.0182467,0.01268354,0.003806506,0.001762309,0.001866601,0.0000669884],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963319,0.00008366702,0.0008739831,0.0001717178,0.00001749346,0.00003090379,0.00008816493,0.000006358757,0.002395845],"genre_scores_gemma":[0.9990647,0.00005681537,0.0004444468,0.00004595594,0.000005780992,0.00002179278,0.00006691091,0.00000347631,0.0002900118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005278299,"threshold_uncertainty_score":0.015697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03793862058407723,"score_gpt":0.3614168883513453,"score_spread":0.3234782677672681,"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."}}