{"id":"W7083582920","doi":"10.1016/j.labeco.2025.102805","title":"Breaking barriers: The impacts of employer exposure to immigrants","year":2025,"lang":"en","type":"article","venue":"Labour Economics","topic":"Research in Cotton Cultivation","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Fundação para a Ciência e a Tecnologia; Social Sciences and Humanities Research Council; Social Sciences and Humanities Research Council of Canada; Centro de Ecológia Aplicada","keywords":"Immigration; Job loss; Displaced workers; Migrant workers; Work (physics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003232401,0.0000667836,0.0001035583,0.00001332273,0.0001219781,0.00006846349,0.0003099611,0.00004799209,0.0001827609],"category_scores_gemma":[0.0001991098,0.00002283089,0.00004774649,0.0002377418,0.00003118091,0.00009983873,0.00009084785,0.00006864467,0.00001861433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003530328,"about_ca_system_score_gemma":0.0000233073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001115272,"about_ca_topic_score_gemma":0.002227691,"domain_scores_codex":[0.9994006,0.0000433135,0.0001772557,0.000143917,0.00005100258,0.0001839364],"domain_scores_gemma":[0.9995192,0.000208122,0.00005589539,0.00007910255,0.00006556539,0.00007214727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001392659,0.00005065892,0.5006511,0.00003379131,0.00008936944,8.539625e-7,0.0007484036,0.000517202,0.2884801,0.04897035,0.003569261,0.1567496],"study_design_scores_gemma":[0.000119964,0.0001145242,0.9043778,0.00003530524,0.000003379638,7.02503e-7,0.0007251038,0.00003228525,0.0257427,0.006821832,0.06193089,0.0000955042],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900777,0.00004343804,0.000004959146,0.008970252,0.00009866633,0.0001798579,0.00004687998,0.00001458376,0.000563637],"genre_scores_gemma":[0.9980755,0.00004792005,0.00006014278,0.001206397,0.00007867569,0.00001147782,0.000004797737,5.656378e-7,0.0005144574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4037267,"threshold_uncertainty_score":0.2001104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0163884692513412,"score_gpt":0.2593368502187203,"score_spread":0.2429483809673791,"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."}}