{"id":"W1892375240","doi":"10.14240/jmhs.v3i4.54","title":"The US Eligible-to-Naturalize Population: Detailed Social and Economic Characteristics","year":2015,"lang":"en","type":"article","venue":"Journal on Migration and Human Security","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Population; Psychology; Economics; Sociology; Demography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005014082,0.0001551631,0.0001438186,0.001641776,0.0005332347,0.0005055772,0.0003081285,0.0002240484,0.003283114],"category_scores_gemma":[0.001573171,0.0001181925,0.0002559984,0.002089054,0.000191802,0.0006394525,0.0007776942,0.000468437,0.0008673688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003510959,"about_ca_system_score_gemma":0.000821297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04881007,"about_ca_topic_score_gemma":0.05781378,"domain_scores_codex":[0.9997237,0.00004517802,0.00004021743,0.0000362011,0.00009306351,0.00006165524],"domain_scores_gemma":[0.9992958,0.00006391028,0.000260356,0.00005226014,0.0002037226,0.0001238412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002747112,0.00008510924,0.9714612,0.00003496202,0.00003293512,0.00008549925,0.0007666965,0.0003378112,0.0001857064,0.001078264,0.01156548,0.01433901],"study_design_scores_gemma":[0.000002462725,0.00002946149,0.9882441,0.00002286547,0.00001089575,0.0001487321,0.00124227,0.0003040413,0.00008043373,0.0002756376,0.009630276,0.000008809625],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.942364,0.0004888646,0.001200394,0.0009414615,0.00003052401,0.0002041297,0.03844115,0.0000438843,0.01628553],"genre_scores_gemma":[0.9515648,0.001047246,0.001309468,0.00051144,0.00004801145,0.0004293157,0.04092029,0.00002333616,0.004146136],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04881007,"threshold_uncertainty_score":0.09705192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02879309789838894,"score_gpt":0.3324491944995762,"score_spread":0.3036560966011873,"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."}}