{"id":"W3137581224","doi":"","title":"All that Glitters is Not Gold: Wages and Education for Us Immigrants","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Immigration; Quarter (Canadian coin); Wage; Variance (accounting); Demographic economics; Quality (philosophy); Economics; Distribution (mathematics); Labour economics; Geography; Mathematics","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":[0.001040048,0.0001367925,0.0003011556,0.001094173,0.0008873465,0.00116846,0.0003958955,0.0007650545,0.003121051],"category_scores_gemma":[0.007206024,0.0001116374,0.0002782352,0.002701641,0.0005790408,0.0008163447,0.001228765,0.0009319746,0.0004450096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007918959,"about_ca_system_score_gemma":0.0009208009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08101264,"about_ca_topic_score_gemma":0.1393692,"domain_scores_codex":[0.9995208,0.0001655514,0.00003168644,0.00005355699,0.00006822368,0.0001601218],"domain_scores_gemma":[0.9975085,0.0006765703,0.000968127,0.0001428896,0.0002323308,0.0004714303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001246416,0.0001235328,0.9823705,0.00001532187,0.00003968681,0.00009769348,0.001337616,0.0007170579,0.00005216006,0.00170803,0.002975652,0.01043808],"study_design_scores_gemma":[0.00002067598,0.00008542064,0.9882433,0.00005973123,0.00002292655,0.00005378829,0.006458936,0.001095384,0.00007702172,0.001172664,0.002700324,0.000009958586],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954047,0.0002663825,0.00008016337,0.001739463,0.00001440854,0.000003783821,0.0006616456,0.000004001187,0.001825496],"genre_scores_gemma":[0.9976758,0.0001716558,0.00005998532,0.0001454918,0.00001629953,0.000005904866,0.000830168,0.000003716907,0.001090825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08101264,"threshold_uncertainty_score":0.1610822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01020547971289202,"score_gpt":0.289401257328074,"score_spread":0.279195777615182,"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."}}