{"id":"W4398510877","doi":"10.7910/dvn/iceycr","title":"Replication Data for: Using Earnings Calls to Understand the Political Behavior of Major Polluters","year":2021,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Replication (statistics); Earnings; Politics; Political science; Data science; Economics; Computer science; Accounting; Statistics; Law; Mathematics","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":[0.003064397,0.001224563,0.001081365,0.002222679,0.001302595,0.002438542,0.002594237,0.001907217,0.1562568],"category_scores_gemma":[0.02210027,0.0007949228,0.001192688,0.004066108,0.0004136385,0.001603588,0.002089013,0.002286499,0.1179236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001727446,"about_ca_system_score_gemma":0.003030215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03855308,"about_ca_topic_score_gemma":0.06731136,"domain_scores_codex":[0.9976593,0.000481392,0.0003341209,0.0005454777,0.0006723691,0.0003074129],"domain_scores_gemma":[0.9878115,0.002530361,0.00126532,0.003311565,0.004466049,0.0006152943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005140346,0.00002086916,0.0009107263,0.00009739855,0.00001274935,0.000007345569,0.00001696528,0.00006667364,0.00003227311,0.0003628552,0.9972714,0.001149357],"study_design_scores_gemma":[0.0008037601,0.00004365835,0.02239851,0.0002997362,0.00006929453,0.00004353147,0.0002294959,0.0004231241,0.0005839363,0.002217838,0.9728107,0.00007631222],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003306296,0.00001805728,0.0001122572,0.0001475642,0.00006323972,0.00004459602,0.9975637,0.0001979317,0.001522021],"genre_scores_gemma":[0.001563199,0.0000211234,0.0004541863,0.0001319915,0.00003594548,0.0003742196,0.9927005,0.0001687587,0.004550154],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1562568,"threshold_uncertainty_score":0.522731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1343872277235191,"score_gpt":0.2943782664153034,"score_spread":0.1599910386917843,"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."}}