{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002721316,0.0001516711,0.0002360281,0.0001377795,0.002175615,0.001069566,0.0003802046,0.0001792997,0.00001417764],"category_scores_gemma":[0.001413847,0.0001549106,0.00005812864,0.0004691795,0.0002150679,0.001054668,0.0003522174,0.0002048684,0.00001242869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001415349,"about_ca_system_score_gemma":0.0001255766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005342776,"about_ca_topic_score_gemma":0.00004963487,"domain_scores_codex":[0.9982564,0.0001066992,0.0005577267,0.0001200145,0.0004389271,0.0005202689],"domain_scores_gemma":[0.9990096,0.00009452683,0.0004117539,0.0002344307,0.0001747577,0.00007493466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002209128,0.00006671609,0.001170559,0.001138318,0.00008327672,5.860817e-7,0.6022904,0.000004271785,0.0005239832,0.15599,0.002086264,0.2366235],"study_design_scores_gemma":[0.003758657,0.00022465,0.01233754,0.0007928113,0.0003098663,0.000008302509,0.2795052,0.1023693,0.009418984,0.2925442,0.2970227,0.001707815],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9273423,0.0001022121,0.06412896,0.004181811,0.0004097361,0.0009216677,0.00007906,0.0001509272,0.002683333],"genre_scores_gemma":[0.9922634,0.0002197997,0.005884908,0.0006355013,0.000227909,0.00004594852,0.00003476441,0.000009549556,0.0006782355],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3227853,"threshold_uncertainty_score":0.9999674,"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."}}