{"id":"W4289516857","doi":"10.1186/s40878-022-00305-0","title":"An eye for an ‘I:’ a critical assessment of artificial intelligence tools in migration and asylum management","year":2022,"lang":"en","type":"article","venue":"Comparative Migration Studies","topic":"Migration, Health and Trauma","field":"Psychology","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Refugee; Government (linguistics); Immigration; Big data; Asylum seeker; Computer science; Identification (biology); Artificial intelligence; National security; Biometrics; Computer security; Political science; Law","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":[],"consensus_categories":[],"category_scores_codex":[0.07723214,0.002273841,0.001750779,0.01331642,0.01146667,0.03700313,0.005129045,0.02374553,0.004752596],"category_scores_gemma":[0.116887,0.001429214,0.001667856,0.00682668,0.06145489,0.06481463,0.01289144,0.03490172,0.001555508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01427457,"about_ca_system_score_gemma":0.01654181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0119602,"about_ca_topic_score_gemma":0.01055866,"domain_scores_codex":[0.9173622,0.06263368,0.003491856,0.00246507,0.01197997,0.002067369],"domain_scores_gemma":[0.7979625,0.1692232,0.003741103,0.005153789,0.02030335,0.003616017],"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":[0.0001167054,0.0001140931,0.001199006,0.003036236,0.00009460474,0.0004204002,0.02105028,0.0007892298,0.0003884551,0.5637882,0.264103,0.1448998],"study_design_scores_gemma":[0.0000230686,0.0001312269,0.0007754972,0.009861334,0.00003442953,0.0004645744,0.03384589,0.0009373266,0.0003532958,0.2317423,0.7216978,0.0001331787],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.001123268,0.1158978,0.004425358,0.8546714,0.007741879,0.00006501673,0.00003142903,0.00005336525,0.01599052],"genre_scores_gemma":[0.1523939,0.3431269,0.0351535,0.4192065,0.03507043,0.0006010338,0.0001072699,0.0003971729,0.01394323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07723214,"threshold_uncertainty_score":0.4084475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3129151216021187,"score_gpt":0.5419333040569383,"score_spread":0.2290181824548196,"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."}}