{"id":"W4214549042","doi":"10.31234/osf.io/bhpm5","title":"Neither nature nor nurture: Using extended pedigree data to understand indirect genetic effects on offspring educational outcomes","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Cognitive Abilities and Testing","field":"Psychology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Child Health and Human Development; Norwegian Institute of Public Health; National Institutes of Health; Helse Vest; ZonMw; Jacobs Foundation; Sage Foundation; Helse- og Omsorgsdepartementet; Trond Mohn stiftelse; University of Texas at Austin; Stiftelsen Kristian Gerhard Jebsen; Universitetet i Bergen; Novo Nordisk; Novo Nordisk Fonden; Norges Forskningsråd; Canadian Institute for Advanced Research; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Nature versus nurture; Offspring; Educational attainment; Nuclear family; Norwegian; Inheritance (genetic algorithm); Developmental psychology; Affect (linguistics); Biology; Genetics; Psychology; Gene; Pregnancy; Sociology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.006086455,0.000421367,0.000401593,0.001482024,0.0004124712,0.0007396286,0.000387817,0.0002734353,0.001039115],"category_scores_gemma":[0.01644768,0.0003149066,0.0005053385,0.001755825,0.000546867,0.001245365,0.0009291791,0.0005228297,0.0001288545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000260087,"about_ca_system_score_gemma":0.0004879571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009758297,"about_ca_topic_score_gemma":0.01868055,"domain_scores_codex":[0.9968741,0.002396436,0.00009875449,0.0003768727,0.0001542278,0.00009954118],"domain_scores_gemma":[0.9855643,0.009726162,0.001714046,0.002308424,0.0002410457,0.0004459464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008149488,0.00002095962,0.9878127,0.00002082739,0.0004481817,0.0001301224,0.0004076569,0.0007408757,0.0004652142,0.0004739486,0.00006619676,0.009331819],"study_design_scores_gemma":[0.00001579925,0.00007809154,0.9929861,0.00003126447,0.0002607631,0.0002178113,0.0002698083,0.003434788,0.0001540192,0.001981737,0.0005586286,0.0000112267],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938807,0.0004433548,0.004693587,0.00007270809,0.000007609565,0.000007505163,0.0003285966,0.000007426828,0.0005583627],"genre_scores_gemma":[0.9965872,0.0002349224,0.002542196,0.00002349106,0.000009805726,0.00001138641,0.0004645496,0.000006753822,0.0001195885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009758297,"threshold_uncertainty_score":0.03218865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.125798897514358,"score_gpt":0.4053610700486456,"score_spread":0.2795621725342876,"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."}}