{"id":"W7122783535","doi":"10.1093/migration/mnaf058","title":"Competing globally, marketing locally: Subnational migration marketing in Australia and Canada","year":2025,"lang":"en","type":"article","venue":"Migration Studies","topic":"Migration, Ethnicity, and Economy","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Fonds de recherche du Québec","keywords":"Immigration; Leverage (statistics); Distribution (mathematics); Population; Competition (biology)","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":[0.001542228,0.00029799,0.0003406431,0.001125567,0.01433548,0.005239408,0.000789962,0.0005225978,0.005437588],"category_scores_gemma":[0.002260107,0.0001928077,0.0001914738,0.00239033,0.004016093,0.00171254,0.003047636,0.001286864,0.0002131628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04149348,"about_ca_system_score_gemma":0.0840448,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9681157,"about_ca_topic_score_gemma":0.989077,"domain_scores_codex":[0.9987075,0.0003075886,0.00002730137,0.0001371231,0.0003278779,0.0004926205],"domain_scores_gemma":[0.9970085,0.0004741291,0.0002080476,0.00008234545,0.0008093018,0.001417811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001682932,0.0004210185,0.1436081,0.0003658674,0.00003469536,0.001700802,0.6977605,0.000292897,0.002195294,0.01171463,0.009534625,0.1322033],"study_design_scores_gemma":[0.000007498731,0.00009461677,0.1744761,0.0001768242,0.00001938806,0.0002000036,0.7437997,0.0005277597,0.000451452,0.0006299552,0.0795595,0.00005709238],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9757242,0.0004061279,0.0002205757,0.001940736,0.00002749506,0.00006833435,0.00005505348,0.00001413738,0.02154337],"genre_scores_gemma":[0.9875607,0.0006548263,0.0006621911,0.000652316,0.000004672156,0.00002879995,0.00004645948,0.00001758435,0.01037254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04149348,"threshold_uncertainty_score":0.3010576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03307148277093605,"score_gpt":0.3232531877630086,"score_spread":0.2901817049920725,"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."}}