{"id":"W4245305366","doi":"10.1177/0197918318781832","title":"Selections Before the Selection","year":2018,"lang":"en","type":"article","venue":"International Migration Review","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Statistics Canada","funders":"","keywords":"Selection (genetic algorithm); Geography; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004437048,0.001307437,0.002439753,0.007830564,0.002996073,0.007989805,0.001967721,0.002830469,0.3279789],"category_scores_gemma":[0.03301035,0.0006381028,0.001308863,0.005209353,0.0007802641,0.003435419,0.00327707,0.003430893,0.2948739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001946655,"about_ca_system_score_gemma":0.00389708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002117224,"about_ca_topic_score_gemma":0.006765142,"domain_scores_codex":[0.9957301,0.0006626122,0.0004659405,0.0005125948,0.002059781,0.0005689856],"domain_scores_gemma":[0.9730386,0.0022276,0.0008214292,0.001423241,0.01926141,0.00322777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00002186906,0.00000409832,0.00001974387,0.0001268361,0.000002173594,0.00001836043,0.00001120576,0.000006291639,0.00008296069,0.0006663242,0.9871296,0.01191055],"study_design_scores_gemma":[0.00001076918,0.000009639824,0.0002110381,0.0002262936,0.000003992056,0.00002601929,0.000043903,0.00001182317,0.00006993476,0.0004765749,0.9989017,0.000008285722],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.0006349136,0.01758763,0.002165143,0.0471389,0.7685589,0.000894865,0.007179061,0.001168774,0.1546719],"genre_scores_gemma":[0.006975055,0.0154649,0.002151721,0.02763566,0.1355534,0.001502166,0.008321662,0.001983,0.8004124],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3279789,"threshold_uncertainty_score":0.9585565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01792405888168777,"score_gpt":0.3571606487877896,"score_spread":0.3392365899061018,"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."}}