{"id":"W4414798578","doi":"10.1093/analys/anaf066","title":"Immigrant Selection and Global Subordination: A Critical Response to Sahar Akhtar","year":2025,"lang":"en","type":"article","venue":"Analysis","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Immigration; Selection bias","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006444128,0.00004787078,0.0001070182,0.0001941903,0.000330962,0.00009935066,0.00006264553,0.00005241407,0.0002061628],"category_scores_gemma":[0.0008494218,0.00004867569,0.00006481809,0.003475398,0.00004964238,0.00008033924,0.00001413126,0.00003535074,0.00001130835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000119478,"about_ca_system_score_gemma":0.00009503542,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003269477,"about_ca_topic_score_gemma":0.174649,"domain_scores_codex":[0.9991739,0.000252573,0.0001115793,0.0001615651,0.000167735,0.0001325913],"domain_scores_gemma":[0.9995138,0.0001398084,0.00001395026,0.00007064829,0.0001784661,0.00008329775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0004807532,0.0001322328,0.2004931,0.00001410151,0.000742142,0.00000645153,0.01381005,0.0007217509,0.0006582682,0.7616664,0.006805002,0.01446982],"study_design_scores_gemma":[0.0004298506,0.0001008762,0.6448347,0.00002248486,0.00203244,7.472971e-7,0.01773497,0.0431643,0.00007704132,0.00550556,0.2857014,0.0003956617],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9218959,0.000124432,0.03101186,0.04289192,0.00007035206,0.00008955105,0.00001567871,0.00006064897,0.003839696],"genre_scores_gemma":[0.9953295,0.00002929508,0.0005197156,0.001012588,0.00002393533,0.000006629129,0.000003671748,0.000001140695,0.003073529],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7561608,"threshold_uncertainty_score":0.8404115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00622785216199814,"score_gpt":0.3458461407904266,"score_spread":0.3396182886284285,"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."}}