{"id":"W2091827604","doi":"10.1080/1369118x.2011.595816","title":"America Identified: Biometric Technology and Society","year":2011,"lang":"en","type":"article","venue":"Information Communication & Society","topic":"Names, Identity, and Discrimination Research","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Biometrics; Political science; Media studies; Sociology; Computer science; Artificial intelligence","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":[0.001964481,0.002228639,0.001155811,0.006384581,0.001819206,0.007217653,0.001027938,0.004163622,0.06147733],"category_scores_gemma":[0.003946179,0.0009250895,0.0003698264,0.01021735,0.003232392,0.01800606,0.0042787,0.004157337,0.02638512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00342025,"about_ca_system_score_gemma":0.002800457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01505299,"about_ca_topic_score_gemma":0.03340933,"domain_scores_codex":[0.9985784,0.0004810279,0.0001281608,0.0001910279,0.0005081874,0.0001131237],"domain_scores_gemma":[0.9979106,0.0007867466,0.0002560345,0.0001966977,0.0005512738,0.0002987085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001973653,0.000008880652,0.0003587817,0.0002342043,0.000008312755,0.0000345178,0.000459755,0.00004350217,0.00008763352,0.01387349,0.9174015,0.06746962],"study_design_scores_gemma":[0.000005406804,0.00000933732,0.002217501,0.0009259193,0.000006578709,0.0001576833,0.001159373,0.00005299364,0.00005081454,0.01091317,0.9844863,0.00001484853],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.0005664901,0.8433924,0.001515525,0.06557203,0.01293475,0.00002089586,0.001368551,0.000346408,0.07428292],"genre_scores_gemma":[0.03018579,0.718593,0.003791799,0.01880198,0.02405735,0.0001349792,0.003543063,0.0003984644,0.2004936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9981808,"threshold_uncertainty_score":0.2056622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05946664865717759,"score_gpt":0.3417411206432659,"score_spread":0.2822744719860883,"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."}}