{"id":"W4391792314","doi":"10.1016/j.socscimed.2024.116683","title":"“The machine doesn't judge”: Counternarratives on surveillance among people accessing a safer opioid supply via biometric machines","year":2024,"lang":"en","type":"article","venue":"Social Science & Medicine","topic":"HIV, Drug Use, Sexual Risk","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Paul's Hospital; British Columbia Centre on Substance Use; University of Waterloo; University of British Columbia","funders":"","keywords":"Harm; SAFER; Internet privacy; Confidentiality; Harm reduction; Computer security; Grounded theory; Public health; Medicine; Biometrics; Computer science; Qualitative research; Artificial intelligence; Psychology; Social psychology; Nursing; Sociology","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":[0.008862374,0.0006121268,0.000736425,0.001259646,0.01678003,0.00604661,0.002357633,0.00494676,0.003928966],"category_scores_gemma":[0.02697036,0.0008967364,0.0005484586,0.001139196,0.01629134,0.007545338,0.007797566,0.008900424,0.0004552612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006031309,"about_ca_system_score_gemma":0.006938717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05333632,"about_ca_topic_score_gemma":0.06863738,"domain_scores_codex":[0.9897617,0.006539273,0.0003933295,0.0005293059,0.001148896,0.001627606],"domain_scores_gemma":[0.9833455,0.009783092,0.002756047,0.000706027,0.001779862,0.001629477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002303566,0.00003014079,0.003788571,0.000110249,0.000006694961,0.0007257577,0.9871653,0.00001113143,0.0003557945,0.0009680711,0.001510722,0.005304441],"study_design_scores_gemma":[0.000005689782,0.00006481185,0.002774086,0.0002611041,0.000008686778,0.0005927552,0.9832476,0.00004451082,0.0001291041,0.0002720382,0.01258124,0.00001851189],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9595482,0.001669549,0.001281718,0.02456455,0.0003831488,0.0001247881,0.00004597915,0.0000401855,0.01234186],"genre_scores_gemma":[0.9818325,0.001414476,0.0007643398,0.01214764,0.00008324577,0.000107871,0.0000349424,0.00003594784,0.003579093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05333632,"threshold_uncertainty_score":0.1060517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02512315153267811,"score_gpt":0.3671509681671162,"score_spread":0.3420278166344382,"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."}}