{"id":"W2139207950","doi":"","title":"ACCOUNTING FOR ACHIEVEMENT IN ATHENS: A COUNT DATA ANALYSIS OF NATIONAL OLYMPIC PERFORMANCE","year":2006,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Count data; Medal; Gross domestic product; Negative binomial distribution; Per capita; Poisson regression; Statistics; Econometrics; Geography; Demography; Population; Covariate; Mathematics; Poisson distribution; Economics; Economic growth; Sociology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.007388094,0.0004021817,0.001460205,0.003011557,0.0001061207,0.0001273131,0.00192945,0.0004601961,0.0001103233],"category_scores_gemma":[0.0004410753,0.0005581292,0.0003086127,0.0004810049,0.0002458549,0.0003688761,0.002003315,0.0008208645,0.00001630956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003120878,"about_ca_system_score_gemma":0.0003114199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008551038,"about_ca_topic_score_gemma":0.002102505,"domain_scores_codex":[0.9944997,0.0000647671,0.002465178,0.001927041,0.0001559673,0.0008873293],"domain_scores_gemma":[0.9959844,0.000550382,0.001315164,0.001996639,0.00006617641,0.00008727006],"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":[0.00006827823,0.0004735407,0.6516496,0.0002939273,0.0008441753,0.00000164223,0.0002084213,0.3315939,0.00001085087,0.00856363,0.00005724028,0.006234843],"study_design_scores_gemma":[0.001094826,0.00005099081,0.4220499,0.0000981676,0.00003710728,5.393769e-7,0.00008532454,0.5527479,0.00001893637,0.005947545,0.01729194,0.0005768952],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9661408,0.0004982401,0.0001149366,0.0002100264,0.0002727808,0.0009398749,0.00357651,0.00001536159,0.02823146],"genre_scores_gemma":[0.9885574,0.005333751,0.002331364,0.00008713292,0.0001747393,0.0004916177,0.002292828,0.00008684623,0.000644314],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2295997,"threshold_uncertainty_score":0.999687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07026050189610758,"score_gpt":0.298699504978199,"score_spread":0.2284390030820914,"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."}}