{"id":"W2530441853","doi":"","title":"Immigrant Women and Retirement","year":2004,"lang":"fr","type":"article","venue":"Retraite et société","topic":"Aging, Elder Care, and Social Issues","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Immigration; Demographic economics; Population; Quarter (Canadian coin); Political science; Sociology; Demography; Geography; Economics","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":[0.001026856,0.0002019203,0.0002751786,0.0008874375,0.003562959,0.001403531,0.0003506053,0.001210497,0.009673688],"category_scores_gemma":[0.003202328,0.0001286104,0.0003245769,0.0007907807,0.0009223441,0.0007999193,0.002132744,0.0008014078,0.0004849884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006474004,"about_ca_system_score_gemma":0.001665859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01308031,"about_ca_topic_score_gemma":0.02167336,"domain_scores_codex":[0.9994854,0.000197859,0.00002984217,0.0000359015,0.0000479776,0.000203017],"domain_scores_gemma":[0.9991511,0.0001684626,0.0001969368,0.00002757927,0.00006870182,0.0003872191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002605027,0.0005072595,0.77774,0.0004705577,0.0000903726,0.004591837,0.1100786,0.00004974668,0.0002963241,0.007934483,0.01935934,0.07862096],"study_design_scores_gemma":[0.00003738869,0.000464352,0.6716391,0.001406008,0.0001055897,0.002970644,0.2522283,0.0001000785,0.00007052356,0.002354581,0.06858031,0.0000430006],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9556,0.01053528,0.00004298161,0.01080491,0.00041775,0.0000282926,0.0001363116,0.000006286577,0.02242816],"genre_scores_gemma":[0.9871616,0.00574498,0.0000400904,0.00176659,0.0001577412,0.000017494,0.00007273597,0.000002451038,0.005036352],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01308031,"threshold_uncertainty_score":0.03236169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03836803399830366,"score_gpt":0.3936278403762161,"score_spread":0.3552598063779124,"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."}}