{"id":"W4282960602","doi":"10.1017/dem.2022.14","title":"Legally ever after: How did 1986 immigration reform affect marriage?","year":2022,"lang":"en","type":"article","venue":"Journal of Demographic Economics","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Immigration; License; Affect (linguistics); Demographic economics; Educational attainment; Natural experiment; Immigration reform; Political science; Economics; Immigration law; Psychology; Law; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00239009,0.0000916047,0.0004471206,0.0006221593,0.001212102,0.002059149,0.0006539436,0.0007374883,0.00723697],"category_scores_gemma":[0.01395041,0.0001485422,0.0003489349,0.0008719861,0.0008023428,0.0007525109,0.001080151,0.001482801,0.0005077549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001659476,"about_ca_system_score_gemma":0.001037767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05899662,"about_ca_topic_score_gemma":0.08890455,"domain_scores_codex":[0.998529,0.0006408298,0.0000700066,0.0001281845,0.0001827997,0.0004491711],"domain_scores_gemma":[0.9897502,0.003210451,0.004663137,0.0002780512,0.0008313268,0.001266853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006193846,0.0008814488,0.9780222,0.00003030516,0.00009295861,0.0001713375,0.001484795,0.000600073,0.0002654595,0.002374569,0.001324355,0.01413311],"study_design_scores_gemma":[0.00001136879,0.0001316854,0.9962128,0.00001634105,0.00002589076,0.0000158576,0.001681914,0.0003013338,0.0001387361,0.0001848574,0.001273816,0.000005337539],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940169,0.0003972595,0.00005000089,0.001990285,0.00003093442,0.00001343197,0.0002359836,0.000002711015,0.003262541],"genre_scores_gemma":[0.9981543,0.0001376473,0.00001610486,0.0001717506,0.0000299346,0.000005103109,0.0001778786,0.000001400394,0.001305738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05899662,"threshold_uncertainty_score":0.1173064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007718467235666073,"score_gpt":0.2328721330310914,"score_spread":0.2251536657954254,"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."}}