{"id":"W4401642149","doi":"10.1177/20539517241274593","title":"Interoperable and standardized algorithmic images: The domestic war on drugs and mugshots within facial recognition technologies","year":2024,"lang":"en","type":"article","venue":"Big Data & Society","topic":"Global Security and Public Health","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Interoperability; Facial recognition system; Data science; Artificial intelligence; Pattern recognition (psychology); World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.01234983,0.0003540119,0.0003165383,0.002175312,0.002288253,0.007990689,0.00132901,0.001463007,0.003938979],"category_scores_gemma":[0.02867532,0.0003544925,0.0003149366,0.002813685,0.005581889,0.01107711,0.007631503,0.002336963,0.001567086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003010877,"about_ca_system_score_gemma":0.002482368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008884245,"about_ca_topic_score_gemma":0.009673692,"domain_scores_codex":[0.9882493,0.00507008,0.0008034389,0.001344144,0.003915751,0.0006172862],"domain_scores_gemma":[0.9825625,0.003710708,0.001156612,0.009872503,0.002259154,0.0004384353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004161099,0.0001355571,0.01398401,0.0002469166,0.00004149846,0.000333712,0.01194473,0.002237278,0.004587234,0.3841777,0.09507139,0.4868237],"study_design_scores_gemma":[0.00002928056,0.0001269408,0.0198033,0.000772986,0.00003351198,0.0008245026,0.0205304,0.00730046,0.0157985,0.1766156,0.7580293,0.0001352508],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2711719,0.006161811,0.3186986,0.08225045,0.002616756,0.000935714,0.01682299,0.00434295,0.2969988],"genre_scores_gemma":[0.7857217,0.002631978,0.1623067,0.01076951,0.000706788,0.0005872138,0.01556009,0.001217225,0.02049878],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9977117,"threshold_uncertainty_score":0.06531292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07127892179494628,"score_gpt":0.3495039233429084,"score_spread":0.2782250015479621,"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."}}