{"id":"W2906087709","doi":"10.32920/ryerson.14656512","title":"Biometrics: constructing 'ideal' subjects and 'aliens' at the Canada-U.S. border","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Migration, Refugees, and Integration","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Saskatchewan","funders":"","keywords":"Citizenship; Biometrics; Argument (complex analysis); Refugee; Context (archaeology); Immigration; Population; Ideal (ethics); Political science; Position (finance); Face (sociological concept); Sociology; Geography; Law; Computer security; Business; Computer science; Social science; Demography; Politics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.01445749,0.0008439316,0.0005331176,0.003794997,0.04530795,0.02331036,0.001918283,0.003240228,0.002250834],"category_scores_gemma":[0.009872241,0.0004107622,0.0003009765,0.004637545,0.1185638,0.007774935,0.01038589,0.005695696,0.0001645491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09280956,"about_ca_system_score_gemma":0.1003535,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8855622,"about_ca_topic_score_gemma":0.8652353,"domain_scores_codex":[0.9890391,0.006011805,0.0001786365,0.0008139589,0.001911628,0.002044903],"domain_scores_gemma":[0.9930265,0.003651127,0.0007759513,0.0003496301,0.001106978,0.001089802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001153297,0.000006700608,0.001402224,0.00002312253,0.000001690926,0.0002005891,0.7005907,0.00007108798,0.0001858377,0.2934963,0.0009788567,0.003031401],"study_design_scores_gemma":[0.000004219713,0.00000671061,0.001584755,0.0001573153,0.000005929852,0.00005764226,0.9232985,0.000207372,0.0002033715,0.01968015,0.05477253,0.00002146934],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6911076,0.003599504,0.01005509,0.05405231,0.0003432019,0.0001424295,0.000173679,0.00006617355,0.2404599],"genre_scores_gemma":[0.9927481,0.0005640554,0.0009627729,0.0007317579,0.00001282286,0.00002430097,0.0000197503,0.00001732894,0.004919221],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9546921,"threshold_uncertainty_score":0.6733836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01773262811817267,"score_gpt":0.3007038918301849,"score_spread":0.2829712637120122,"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."}}