{"id":"W2027850833","doi":"10.1007/s12134-000-1015-3","title":"Economic impacts of immigrants in the Toronto CMA: A tax-benefit analysis","year":2000,"lang":"fr","type":"article","venue":"Journal of International Migration and Integration / Revue de l integration et de la migration internationale","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; Toronto Metropolitan University","funders":"Ryerson University","keywords":"Immigration; Unemployment; Metropolitan area; Demographic economics; Census; Welfare; Economics; Refugee; Income tax; Geography; Sociology; Economic growth; Public economics; Demography; Population","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004865408,0.0003145801,0.0003956014,0.0008778367,0.001301443,0.001519849,0.0006804791,0.0006183555,0.006314413],"category_scores_gemma":[0.002110929,0.0001936778,0.0006915873,0.002170671,0.0007287845,0.0004094183,0.001233905,0.0007586715,0.0002183164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01911896,"about_ca_system_score_gemma":0.01110098,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9331315,"about_ca_topic_score_gemma":0.9592198,"domain_scores_codex":[0.9994437,0.0001460862,0.00001730978,0.00002538268,0.00009179205,0.0002757452],"domain_scores_gemma":[0.9989281,0.0001665653,0.0001646922,0.00003960636,0.000290385,0.0004105752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002625385,0.0004602087,0.8864141,0.0002061222,0.0006629699,0.002560951,0.002496636,0.05369207,0.001049329,0.01833568,0.0115607,0.01993586],"study_design_scores_gemma":[0.000163451,0.0003061535,0.9525419,0.00008081803,0.0005712733,0.000255557,0.01291099,0.02364677,0.0005610376,0.001481472,0.007418356,0.00006230726],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931728,0.0003409123,0.00006933515,0.0009062892,0.00001015837,0.00003864401,0.00163942,0.000004295015,0.003817986],"genre_scores_gemma":[0.9964975,0.0003284067,0.00004607068,0.00005369126,0.000005383012,0.000008502478,0.0005040745,0.000001873952,0.002554585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06686848,"threshold_uncertainty_score":0.1387184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01041523500658199,"score_gpt":0.3044409155273806,"score_spread":0.2940256805207986,"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."}}