{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002516586,0.00005792274,0.00006238813,0.0005250862,0.0002464503,0.0001322276,0.0001887485,0.00003995798,0.0005750854],"category_scores_gemma":[0.0004255859,0.00006270298,0.00007907235,0.0007430445,0.00002010234,0.0001881655,0.0000381652,0.00005771064,0.0002682392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001123209,"about_ca_system_score_gemma":0.0000641906,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.007491084,"about_ca_topic_score_gemma":0.03286581,"domain_scores_codex":[0.9991965,0.00005787055,0.0001823616,0.0001665054,0.000280791,0.000115977],"domain_scores_gemma":[0.9994702,0.0000685955,0.00005382716,0.00008457671,0.0002808187,0.00004195961],"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":[0.0001804142,0.001358559,0.02361019,0.00006557691,0.0005790225,0.00001190702,0.03496795,0.002216584,0.009313053,0.1277351,0.3361025,0.4638591],"study_design_scores_gemma":[0.0001063461,0.00002653027,0.002580181,0.0001400152,0.0001245699,5.855358e-7,0.0259598,0.0018358,0.001425305,0.001467435,0.966185,0.0001484414],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6356167,0.00009592625,0.02121677,0.009305943,0.003399099,0.0003278455,0.000007832997,0.0001050525,0.3299249],"genre_scores_gemma":[0.9262878,0.0003836744,0.0006927541,0.0006248778,0.0005836025,0.0000602717,0.00002183798,0.000003837189,0.07134131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6300824,"threshold_uncertainty_score":0.9991181,"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."}}