{"id":"W3047751523","doi":"10.3386/w22571","title":"A Time to Make Laws and a Time to Fundraise? On the Relation between Salaries and Time Use for State Politicians","year":2016,"lang":"en","type":"preprint","venue":"National Bureau of Economic Research","topic":"Regulation and Compliance Studies","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ewing Marion Kauffman Foundation; National Science Foundation","keywords":"Relation (database); State (computer science); Law; Political science; Mathematics; Computer science; Algorithm; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002543736,0.0001334226,0.0003408052,0.001093663,0.0006797671,0.001952408,0.0006235597,0.001546188,0.0173666],"category_scores_gemma":[0.01898677,0.0001633813,0.0007478943,0.001841358,0.001360847,0.002130112,0.001085041,0.001667089,0.0009839043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008903853,"about_ca_system_score_gemma":0.001647611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01428827,"about_ca_topic_score_gemma":0.01950938,"domain_scores_codex":[0.9985108,0.00048876,0.0001101413,0.0002255971,0.000274287,0.0003904964],"domain_scores_gemma":[0.9616191,0.01647542,0.01540316,0.001232582,0.001328273,0.003941564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003910938,0.0003887046,0.956753,0.00006107098,0.0001813903,0.0001526772,0.002500308,0.0002333814,0.0002727659,0.008966898,0.004732891,0.02536589],"study_design_scores_gemma":[0.00001198108,0.00008868031,0.9915287,0.00006154363,0.00007193731,0.00005001281,0.003676556,0.0001910106,0.00008406114,0.001062382,0.003162802,0.00001023575],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9812044,0.001162237,0.0002550342,0.009608496,0.00009707703,0.000008508244,0.0003935781,0.00001051927,0.007260085],"genre_scores_gemma":[0.9972008,0.0003228047,0.00007955913,0.0005064561,0.00009873402,0.000008140555,0.0001888456,0.000009435936,0.001585059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0173666,"threshold_uncertainty_score":0.05809712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2467966590978387,"score_gpt":0.4153075909575608,"score_spread":0.168510931859722,"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."}}