{"id":"W3203465696","doi":"10.1162/glep_a_00641","title":"Using Earnings Calls to Understand the Political Behavior of Major Polluters","year":2021,"lang":"en","type":"article","venue":"Global Environmental Politics","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Multinational corporation; Earnings; Politics; Corporate governance; Transparency (behavior); Construct (python library); Value (mathematics); Accrual; Business; Public economics; Economics; Accounting; Political science; Finance; Law","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":[0.000103493,0.0001637313,0.0003039754,0.00004310833,0.0001268857,0.00004390929,0.0001969249,0.00009838455,0.000473975],"category_scores_gemma":[0.00004363191,0.0001802065,0.0001448449,0.0001034382,0.0002291293,0.00008178977,0.0002371151,0.0001009075,0.0002789883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009254764,"about_ca_system_score_gemma":0.00002512307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001079038,"about_ca_topic_score_gemma":0.00003917224,"domain_scores_codex":[0.9985031,0.00001631232,0.0004992855,0.0003064918,0.00005070727,0.0006240534],"domain_scores_gemma":[0.9992141,0.00003258347,0.000141773,0.0003476731,0.000004449505,0.0002594249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000007418791,0.0001730325,0.1112733,0.00001915153,0.00005514041,0.00001632369,0.0004288441,0.00009766829,0.0004039622,0.887296,0.0001744835,0.00005474757],"study_design_scores_gemma":[0.003724858,0.0003972296,0.648532,0.00009587147,0.0003399589,0.0006855017,0.03838722,0.002261719,0.0117426,0.2600064,0.03139469,0.00243198],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.984451,0.0002631056,0.000714761,0.0022554,0.0002693366,0.0001559021,0.004342583,0.00001071866,0.007537184],"genre_scores_gemma":[0.9954646,0.00003180232,0.0009523213,0.003152366,0.0001286229,0.000004184271,0.00003163302,0.00001988518,0.0002146186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6272895,"threshold_uncertainty_score":0.7348608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09612217468796379,"score_gpt":0.2742867984744156,"score_spread":0.1781646237864518,"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."}}