{"id":"W4383909669","doi":"10.1080/20551940.2023.2232186","title":"Hearing voices: forensic speaker identification technology and expert listening in the American courtroom","year":2023,"lang":"en","type":"article","venue":"Sound Studies","topic":"Law in Society and Culture","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Identification (biology); Expert witness; Witness; Active listening; Eyewitness identification; Psychology; Speaker identification; Computer science; Law; Speaker recognition; Speech recognition; Political science; Communication","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":[0.01541477,0.0004160776,0.0002790981,0.003031769,0.006845492,0.01148473,0.001810876,0.005712336,0.005675457],"category_scores_gemma":[0.03326734,0.0004218426,0.0002513876,0.00121142,0.0293427,0.01448204,0.005964865,0.004411858,0.0008553898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003171523,"about_ca_system_score_gemma":0.003063063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006438255,"about_ca_topic_score_gemma":0.01073663,"domain_scores_codex":[0.9850308,0.009336237,0.0005063226,0.001271905,0.00318348,0.0006711911],"domain_scores_gemma":[0.9790637,0.01711333,0.001250861,0.0006930947,0.001239033,0.0006400088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001558215,0.0002344724,0.01074482,0.0003771921,0.00002402189,0.00290107,0.2069276,0.0007817179,0.005592739,0.4773108,0.01385112,0.2810985],"study_design_scores_gemma":[0.00005404722,0.0006184427,0.02690599,0.002094948,0.00008085995,0.005836301,0.3910819,0.006253405,0.01420698,0.3794494,0.1730444,0.0003734388],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3571382,0.01689447,0.06050825,0.09258526,0.001088191,0.0001897577,0.0000733722,0.0003684035,0.471154],"genre_scores_gemma":[0.9668377,0.004120003,0.00863764,0.005152925,0.0004201802,0.00005783148,0.00001839945,0.00004552328,0.01470972],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9931545,"threshold_uncertainty_score":0.08152211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06067094785118618,"score_gpt":0.3743820648561882,"score_spread":0.313711117005002,"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."}}