{"id":"W7097034237","doi":"","title":"Intergenerational Mobility and the Informative Content of Surnames","year":2007,"lang":"en","type":"article","venue":"","topic":"Intergenerational and Educational Inequality Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Proxy (statistics); Census; Variety (cybernetics); Educational attainment; Distribution (mathematics); Social mobility; Quarter (Canadian coin); Fertility","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":[0.003395163,0.0003372435,0.0004821176,0.003064589,0.0006478072,0.00168702,0.001018693,0.000710983,0.002796017],"category_scores_gemma":[0.02534128,0.0002893324,0.0004467544,0.004125334,0.001651473,0.002141695,0.001566766,0.0008228911,0.0002752618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007230899,"about_ca_system_score_gemma":0.0003482693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00480519,"about_ca_topic_score_gemma":0.005027417,"domain_scores_codex":[0.9979265,0.001146246,0.0001001439,0.0004621175,0.0002521155,0.0001128458],"domain_scores_gemma":[0.9706543,0.01986946,0.005488617,0.003068519,0.0006468797,0.0002723066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002060061,0.0001598113,0.6741267,0.0001857034,0.0007226756,0.0004772764,0.00449856,0.03604732,0.002460606,0.1464752,0.001887591,0.1327524],"study_design_scores_gemma":[0.00004869898,0.0001678421,0.57227,0.0001734254,0.0003132846,0.001147077,0.001755779,0.1276384,0.002520422,0.2832781,0.01050971,0.0001772769],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8020489,0.0008052505,0.1874613,0.001207829,0.0000411098,0.0000701379,0.001931518,0.0001121872,0.006321768],"genre_scores_gemma":[0.9861451,0.0002082566,0.01229526,0.00004439566,0.00003639489,0.00005121046,0.0004585003,0.000009117919,0.0007518077],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00480519,"threshold_uncertainty_score":0.0179556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.099044932744774,"score_gpt":0.3686999398546011,"score_spread":0.2696550071098271,"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."}}