{"id":"W2108976036","doi":"10.1177/000312240607100206","title":"Infant Mortality, Social Networks, and Subsequent Fertility","year":2006,"lang":"en","type":"article","venue":"American Sociological Review","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Fertility; Demography; Macro; Population; Infant mortality; Linkage (software); Social network (sociolinguistics); Social learning; Demographic economics; Geography; Psychology; Sociology; Economics; Political science; Biology; Computer science","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":[0.001585453,0.0001320263,0.0005853028,0.000008409943,0.0006291965,0.00003217774,0.0001678208,0.00008852033,0.0002598239],"category_scores_gemma":[0.0004069119,0.00009744707,0.000153044,0.0002219879,0.002004201,0.00005808355,0.00007062997,0.0002093391,0.00001418989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001148105,"about_ca_system_score_gemma":0.0001058895,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02794393,"about_ca_topic_score_gemma":0.001711995,"domain_scores_codex":[0.9976936,0.000822758,0.0003922874,0.0002713037,0.0002511877,0.0005688591],"domain_scores_gemma":[0.9991346,0.0003504749,0.0001782951,0.0001138128,0.00005066281,0.0001721731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007255861,0.00008389891,0.7605883,0.0004984139,0.00002293005,0.00001042429,0.0003340257,9.495449e-7,4.092327e-7,0.1587119,0.02158604,0.05815545],"study_design_scores_gemma":[0.00004246525,0.0000197776,0.8021613,0.00007924187,0.00002459585,2.614925e-7,0.0002971337,0.000003176076,4.0515e-8,0.002447896,0.1948037,0.0001204005],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.765341,0.1463132,0.0001998976,0.06409486,0.0002078308,0.0009692261,0.00001783357,0.000223048,0.02263318],"genre_scores_gemma":[0.8768824,0.1009698,0.00006808961,0.02157168,0.000347225,0.00004527818,0.00001072084,0.000005136088,0.00009965975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1732177,"threshold_uncertainty_score":0.9785291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06954472912589711,"score_gpt":0.4116705918219727,"score_spread":0.3421258626960756,"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."}}