{"id":"W3017869166","doi":"10.1016/j.exis.2020.04.009","title":"Consistently unreliable: Oil spill data and transparency discourse","year":2020,"lang":"en","type":"article","venue":"The Extractive Industries and Society","topic":"Natural Resources and Economic Development","field":"Economics, Econometrics and Finance","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Oil spill; Transparency (behavior); Publication; Work (physics); Scale (ratio); Petroleum industry; Niger delta; Business; Public relations; Political science; Law; Geography; Advertising; Petroleum engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003500002,0.0001531742,0.0002851406,0.00001021836,0.0002948059,0.0001315825,0.0002828037,0.0001211761,0.0001611735],"category_scores_gemma":[0.00009137179,0.000122688,0.00004599415,0.000112365,0.0002919741,0.0002602975,0.0002024295,0.0003724934,0.00002281797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002858294,"about_ca_system_score_gemma":0.00003449114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001470416,"about_ca_topic_score_gemma":0.00000587143,"domain_scores_codex":[0.9989516,0.000009804661,0.000338289,0.0004367387,0.00003209686,0.0002314277],"domain_scores_gemma":[0.9992996,0.0001186737,0.0001849673,0.0002511195,0.00001356993,0.0001320925],"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":[0.0003297979,0.0002547923,0.207332,0.0003568384,0.002282265,0.00002462016,0.08559044,0.00007638454,0.0001435937,0.1878007,0.2855481,0.2302605],"study_design_scores_gemma":[0.001121448,0.00008723581,0.03422445,0.00003436356,0.0000470686,0.00001194611,0.02033565,0.005573997,0.00005462515,0.003023871,0.934953,0.0005323942],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.915848,0.01326887,0.0004525093,0.04831586,0.000346514,0.000208267,0.001370907,0.00004385904,0.02014523],"genre_scores_gemma":[0.9906492,0.005826409,0.0002411774,0.001727628,0.0001580799,0.0000066315,0.00004186828,0.00001438442,0.001334573],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6494049,"threshold_uncertainty_score":0.500307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1032396210081996,"score_gpt":0.254998566474181,"score_spread":0.1517589454659815,"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."}}