{"id":"W4411374595","doi":"10.1111/imig.70043","title":"No Exit: Preventing Exit to Prevent Entry","year":2025,"lang":"en","type":"article","venue":"International Migration","topic":"Criminal Justice and Corrections Analysis","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Exit strategy; Business; Marketing","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003346215,0.0003261042,0.0004474468,0.0005881405,0.002483245,0.004372988,0.001227364,0.002404676,0.01343805],"category_scores_gemma":[0.0135382,0.0002137371,0.0005306304,0.0003117812,0.005545211,0.00366747,0.005559629,0.002873946,0.001397389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001132027,"about_ca_system_score_gemma":0.004434058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003572591,"about_ca_topic_score_gemma":0.005423259,"domain_scores_codex":[0.9967783,0.001238185,0.0002230252,0.0003683139,0.0004663239,0.0009259252],"domain_scores_gemma":[0.9917584,0.003399298,0.001570002,0.001412705,0.001072606,0.0007870382],"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":[0.0002388418,0.000196191,0.01239273,0.0003268125,0.00003988748,0.000565059,0.0085007,0.007511635,0.001951393,0.9003087,0.01255731,0.05541075],"study_design_scores_gemma":[0.0002088549,0.001669397,0.03425325,0.003687211,0.0002330229,0.001484544,0.04596106,0.02312653,0.0107377,0.5107702,0.3676385,0.0002297513],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4462194,0.001049705,0.0785348,0.01626808,0.000620409,0.0004002774,0.0001923705,0.0004220368,0.456293],"genre_scores_gemma":[0.9774836,0.0002147419,0.005068417,0.00143066,0.00004197438,0.0001140405,0.00004759224,0.00003951251,0.01555945],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01343805,"threshold_uncertainty_score":0.04495472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01536946476298568,"score_gpt":0.3445299506689763,"score_spread":0.3291604859059907,"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."}}