{"id":"W3125772739","doi":"10.3386/w14312","title":"How Much Does Immigration Boost Innovation?","year":2008,"lang":"en","type":"preprint","venue":"National Bureau of Economic Research","topic":"Migration, Ethnicity, and Economy","field":"Social Sciences","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Immigration; Demographic economics; Economic geography; Geography; Economics; Archaeology","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":[],"consensus_categories":[],"category_scores_codex":[0.00150177,0.0001844299,0.000449783,0.0008465044,0.0005885162,0.001498507,0.0002143418,0.0007647798,0.005720485],"category_scores_gemma":[0.007921252,0.0001454602,0.0007487986,0.0009938689,0.0009354816,0.001132684,0.001120589,0.000577456,0.0004989302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007100906,"about_ca_system_score_gemma":0.0006337545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003876709,"about_ca_topic_score_gemma":0.006848342,"domain_scores_codex":[0.9993179,0.0002478489,0.00003162251,0.00008670158,0.000107335,0.000208705],"domain_scores_gemma":[0.9928234,0.003724085,0.002044386,0.0003797237,0.0003282961,0.0007000966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004107487,0.0002861557,0.9383837,0.0001892648,0.0003101076,0.000282599,0.0006548521,0.002104468,0.001675414,0.00860055,0.001370585,0.04573157],"study_design_scores_gemma":[0.00003615892,0.000317662,0.9859368,0.00008803574,0.0002506394,0.0001369937,0.0009783355,0.0009889576,0.0007242162,0.005516634,0.005011621,0.00001392582],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9767582,0.001355637,0.0002585789,0.002983885,0.00004592964,0.00001202589,0.0002161752,0.00001474428,0.01835484],"genre_scores_gemma":[0.9971926,0.000769973,0.0001188801,0.0003809924,0.00008433252,0.000005788303,0.00007785794,0.000002268998,0.001367445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005720485,"threshold_uncertainty_score":0.01913697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2592401058653806,"score_gpt":0.4896963116924925,"score_spread":0.2304562058271119,"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."}}