{"id":"W4409098885","doi":"10.1016/j.orgdyn.2025.101148","title":"The invisible workforce: How can organizations leverage immigrant talent?","year":2025,"lang":"en","type":"article","venue":"Organizational Dynamics","topic":"Migration, Ethnicity, and Economy","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria; Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Workforce; Leverage (statistics); Immigration; Business; Talent management; Labour economics; Management; Marketing; Economics; Economic growth; Geography; Computer science; Archaeology; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006730722,0.0003973415,0.0003547523,0.001653808,0.006711778,0.01152585,0.001582438,0.00288457,0.01263756],"category_scores_gemma":[0.01452443,0.0002104468,0.0002968707,0.0008884273,0.005331523,0.008944382,0.009715727,0.002662747,0.001230121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002462802,"about_ca_system_score_gemma":0.009283623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007768883,"about_ca_topic_score_gemma":0.01894743,"domain_scores_codex":[0.9962307,0.001532801,0.00003885588,0.00016309,0.0003320157,0.001702507],"domain_scores_gemma":[0.9878374,0.003268932,0.001228863,0.0004591737,0.0008755581,0.006330104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003537376,0.002804965,0.2249093,0.0004688207,0.0001814368,0.001646035,0.1317369,0.001419512,0.002245543,0.2235511,0.04490406,0.3657786],"study_design_scores_gemma":[0.0001152361,0.0006542284,0.0911954,0.002207767,0.0001131585,0.0003376121,0.6467456,0.002620466,0.000974134,0.1589352,0.09595092,0.0001503129],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7636346,0.001307494,0.002371514,0.1026961,0.0005430594,0.00005338624,0.00005950873,0.00005197071,0.1292824],"genre_scores_gemma":[0.9929858,0.0004231947,0.0002982042,0.003209638,0.00007747013,0.00001946289,0.00001137419,0.00001018604,0.002964736],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01263756,"threshold_uncertainty_score":0.04227686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007179992651792303,"score_gpt":0.2435914534606971,"score_spread":0.2364114608089048,"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."}}