{"id":"W1483896154","doi":"","title":"Unobserved Heterogeneity in the Binary Logit Model with Cross-Sectional Data and Short Panels: A Finite Mixture Approach","year":2008,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Econometrics; Logit; Logistic regression; Latent variable; Mixed logit; Variables; Binary number; Latent class model; Statistics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003495407,0.0004185772,0.0008668917,0.0007028668,0.000271481,0.0004267459,0.00224201,0.0005918232,0.00002932045],"category_scores_gemma":[0.0003130926,0.0003719627,0.0001487624,0.0003502076,0.0005469339,0.0003518053,0.002208591,0.002050995,0.00001020832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003167744,"about_ca_system_score_gemma":0.0002355914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001043955,"about_ca_topic_score_gemma":0.001257441,"domain_scores_codex":[0.9958093,0.0001849885,0.001104965,0.002027343,0.0001552796,0.000718191],"domain_scores_gemma":[0.9964848,0.0004288376,0.0002797312,0.002609192,0.00006011251,0.0001373133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001629141,0.0002698867,0.7304828,0.000122326,0.0001894725,0.00004583423,0.0003928006,0.2659091,0.000001671129,0.0003123446,0.00003213634,0.002078693],"study_design_scores_gemma":[0.0004072158,0.00005471112,0.3044837,0.00002644767,0.000007260187,0.00002839885,0.0001231464,0.6913183,0.000001439858,0.001616333,0.001522015,0.0004110479],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796886,0.0008452925,0.000411307,0.0001915683,0.00009612357,0.0007759545,0.004797332,0.00002144068,0.01317235],"genre_scores_gemma":[0.9738977,0.02020998,0.001562502,0.0001460839,0.0001581518,0.0002468342,0.003443893,0.00005396643,0.0002809092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4259992,"threshold_uncertainty_score":0.9998732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2342047652076592,"score_gpt":0.3347466092675784,"score_spread":0.1005418440599192,"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."}}