{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003271302,0.0001315866,0.0002064677,0.0001198422,0.0007533937,0.0001174047,0.0001051035,0.0000676728,0.00005132445],"category_scores_gemma":[0.002165382,0.0001421117,0.00002760197,0.0003402164,0.0001438124,0.0002558523,0.00005411656,0.0001065401,0.000002285241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000487898,"about_ca_system_score_gemma":0.0004402635,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7341769,"about_ca_topic_score_gemma":0.9990253,"domain_scores_codex":[0.9981725,0.0005525721,0.0004356447,0.0002788948,0.0002923892,0.0002679643],"domain_scores_gemma":[0.9985019,0.0009585331,0.0001392543,0.0000753936,0.0002793804,0.00004552594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003947507,0.00002036915,0.9570475,0.00008216422,0.00005181353,0.000002095425,0.01423093,0.0002048657,0.00005317857,0.004183103,0.02251502,0.001569469],"study_design_scores_gemma":[0.0003578536,0.00000715975,0.8691357,0.0002530285,0.0000224797,5.434473e-7,0.08079394,0.001105552,0.00004004125,0.0006148408,0.04741943,0.0002493711],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9848276,0.001004978,0.0001117928,0.008950383,0.0003188422,0.0002454167,0.000006698177,0.00003219634,0.0045021],"genre_scores_gemma":[0.9954912,0.0007761568,0.0004163169,0.0003800607,0.000132597,0.00003365769,0.00001709047,0.000004041582,0.002748891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2648484,"threshold_uncertainty_score":0.5795146,"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."}}