{"id":"W4407314675","doi":"10.2139/ssrn.5128269","title":"Breaking Barriers: The Impacts of Employer Exposure to Immigrants","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Immigration; Demographic economics; Business; Labour economics; Political science; Economics; Law","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":[0.00144128,0.0002598931,0.0004803888,0.0007067729,0.001442931,0.003542332,0.0008598661,0.002517708,0.0255986],"category_scores_gemma":[0.01225142,0.0002261239,0.0007086185,0.001161434,0.0008390757,0.001620054,0.003112492,0.002426661,0.001175798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008166602,"about_ca_system_score_gemma":0.001306728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0312354,"about_ca_topic_score_gemma":0.03122957,"domain_scores_codex":[0.9980716,0.0007906196,0.00005946687,0.0001402352,0.0001260277,0.0008120329],"domain_scores_gemma":[0.9889939,0.004953873,0.002222003,0.0003999598,0.0004725931,0.002957611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002686203,0.002535535,0.9143971,0.0002158926,0.0005122717,0.001056435,0.006659977,0.004718818,0.0009456848,0.01538677,0.006642575,0.04424288],"study_design_scores_gemma":[0.0001225344,0.000710052,0.9586143,0.0001881697,0.0002953852,0.0001174799,0.02639588,0.002080131,0.0004072143,0.006544566,0.00448315,0.00004101684],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878015,0.0007755457,0.00009114078,0.004606102,0.0000597354,0.00001324231,0.0005514615,0.000007645036,0.006093697],"genre_scores_gemma":[0.9972501,0.0003263881,0.00001972122,0.0001909924,0.00003542073,0.000006002183,0.0001529151,0.000003884458,0.002014531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0312354,"threshold_uncertainty_score":0.0856359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009158044565763787,"score_gpt":0.3002364999199857,"score_spread":0.2910784553542219,"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."}}