{"id":"W270160519","doi":"10.22237/jmasm/1257035280","title":"Generating and Comparing Aggregate Variables for Use Across Datasets in Multilevel Analysis","year":2009,"lang":"en","type":"article","venue":"Journal of Modern Applied Statistical Methods","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Aggregate (composite); Multilevel model; Econometrics; Mathematics; Statistics; Aggregate data; Data mining; 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":[0.003584146,0.000135463,0.0006218497,0.0001433982,0.0004353539,0.0002184811,0.0001504586,0.00008172198,0.00001006034],"category_scores_gemma":[0.001902341,0.0001171715,0.00006595344,0.0002879701,0.000158838,0.0002127371,0.00003579348,0.0002027911,2.234773e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006753864,"about_ca_system_score_gemma":0.00006251689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00015001,"about_ca_topic_score_gemma":0.0004139194,"domain_scores_codex":[0.9980728,0.0003962075,0.0006501501,0.0002199181,0.0003114876,0.0003494271],"domain_scores_gemma":[0.9958632,0.00336074,0.0003570793,0.0001031658,0.0001450229,0.000170809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002776143,0.0001778185,0.007969881,0.00002217968,0.0004586477,0.00001506959,0.01562975,0.005956283,0.001748452,0.06629454,0.0003648699,0.9010849],"study_design_scores_gemma":[0.001660437,0.00008108575,0.03007264,0.00003071468,0.0004724429,0.00000305066,0.001667533,0.8501399,0.0001155869,0.1136081,0.001854164,0.000294327],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02641609,0.0002416676,0.9726119,0.0001145983,0.00006147753,0.0001552659,0.0001981084,0.00000907199,0.0001918413],"genre_scores_gemma":[0.429045,0.0000641353,0.5706822,0.00009681154,0.0000790318,0.000003874776,0.00001190902,0.000004401249,0.0000126735],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9007906,"threshold_uncertainty_score":0.4778115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.134130607975141,"score_gpt":0.4621337735825821,"score_spread":0.328003165607441,"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."}}