{"id":"W4365151865","doi":"10.5210/spir.v2022i0.13093","title":"ARTIFICIAL INTELLIGENCE AS IM/MOBILITY: PRE-LIMIRARY THOUGHTS ON UNDERSTANDING THE USE OF AI IN IMMIGRATION SYSTEMS","year":2023,"lang":"en","type":"article","venue":"AoIR Selected Papers of Internet Research","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Immigration; Citizenship; Refugee; Government (linguistics); Sociology; Political science; Gender studies; Law; 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":[],"consensus_categories":[],"category_scores_codex":[0.004024704,0.0007558697,0.0005136722,0.003719234,0.005582027,0.01656234,0.002211962,0.006324961,0.004239217],"category_scores_gemma":[0.004191043,0.0003179592,0.0005476029,0.003099287,0.06455988,0.01777215,0.004117814,0.01295445,0.0009052184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008397104,"about_ca_system_score_gemma":0.004427911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01566783,"about_ca_topic_score_gemma":0.01286185,"domain_scores_codex":[0.9970151,0.001895464,0.00007901341,0.000315483,0.0003895847,0.0003053148],"domain_scores_gemma":[0.9960052,0.002917367,0.0002017304,0.0002410209,0.0003826146,0.0002520762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000007245409,0.00001038722,0.0002406364,0.00009282325,0.000003760425,0.00004714136,0.009885245,0.0001620085,0.00004818207,0.9782162,0.006040822,0.00524552],"study_design_scores_gemma":[0.000006168109,0.00002569171,0.0007701128,0.0008539893,0.00000709522,0.0001906908,0.01678013,0.001028365,0.0001502969,0.6369126,0.3432473,0.00002753838],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01675734,0.1224936,0.02625119,0.4763036,0.004107977,0.0000722704,0.0001626489,0.0001281556,0.3537233],"genre_scores_gemma":[0.7553923,0.09639943,0.01892909,0.08304221,0.007878033,0.0003080638,0.0001526503,0.0002226913,0.03767565],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01656234,"threshold_uncertainty_score":0.06092554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1898477414163784,"score_gpt":0.4151281787958973,"score_spread":0.2252804373795189,"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."}}