{"id":"W2558445914","doi":"10.1111/imre.12310","title":"Selections Before the Selection: Earnings Advantages of Immigrants Who Were Former Skilled Temporary Foreign Workers in Canada","year":2016,"lang":"en","type":"article","venue":"International Migration Review","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Immigration; Earnings; Demographic economics; Earnings growth; Labour economics; Economics; Political science; Law","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005257488,0.0001141532,0.0001985587,0.00008999672,0.0001891196,0.00002377539,0.000279473,0.00004693044,0.001089142],"category_scores_gemma":[0.0003846435,0.00007047424,0.00008946937,0.0005403698,0.00007716781,0.0003564983,0.00001686401,0.0001124563,0.00001108263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004761897,"about_ca_system_score_gemma":0.001024107,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7522568,"about_ca_topic_score_gemma":0.9971191,"domain_scores_codex":[0.9984326,0.0001984656,0.0005013797,0.0001769862,0.0005198071,0.0001707963],"domain_scores_gemma":[0.9990112,0.0001508621,0.0003194665,0.000107512,0.0003551626,0.00005576612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001247085,0.00002776929,0.9385877,0.00005498374,0.00004576218,0.000001135318,0.001008183,0.00004525192,0.0000317811,0.009392844,0.03195833,0.01883385],"study_design_scores_gemma":[0.0004675345,0.00003974429,0.1861642,0.002699988,0.00003028206,0.000007492403,0.007282875,0.0005695141,0.00004917143,0.0004996166,0.8019631,0.0002265166],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8589021,0.006157952,0.002135121,0.1114816,0.0009419633,0.001866151,0.0001286842,0.00008160873,0.01830484],"genre_scores_gemma":[0.9705575,0.01915122,0.00008593183,0.00104875,0.0001145987,0.00009242919,0.00002781373,0.00001040061,0.008911352],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7700047,"threshold_uncertainty_score":0.999824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008412804397240967,"score_gpt":0.2826013117225094,"score_spread":0.2741885073252684,"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."}}