{"id":"W2547859870","doi":"10.1111/ropr.12201","title":"Mitigating Mistrust? Participation and Expertise in Hydraulic Fracturing Governance","year":2016,"lang":"en","type":"article","venue":"Review of Policy Research","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Hydraulic fracturing; Credibility; Negotiation; Legislature; Citizen journalism; Corporate governance; Spillover effect; Government (linguistics); Public participation; Uncertainty; Public administration; Political science; Process (computing); Public trust; Public relations; Business; Economics; Law; Engineering; Finance","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005814118,0.0000743433,0.0001493889,0.000007050602,0.00004372878,0.000004805809,0.0001344081,0.00003154459,0.0003573575],"category_scores_gemma":[0.0004124288,0.00005157688,0.00002515044,0.0002422283,0.0003365488,0.0001456035,0.0002013318,0.00009162813,0.00005675421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003924411,"about_ca_system_score_gemma":0.00001233894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001389096,"about_ca_topic_score_gemma":0.00003533612,"domain_scores_codex":[0.9987478,0.0001217479,0.0002430032,0.0001990938,0.0003838481,0.0003045405],"domain_scores_gemma":[0.9995505,0.00009596012,0.00006886675,0.0001896364,0.000003362431,0.00009169455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000005979847,0.00004330002,0.1035787,0.0005633457,0.000002565462,0.000004104322,0.0002180576,0.00008195868,0.008294381,0.0001536374,0.0002153069,0.8868387],"study_design_scores_gemma":[0.000578785,0.0001212767,0.9671839,0.008979911,0.000006269105,0.000007647194,0.00005839952,0.002804054,0.004834761,0.0009075511,0.01423809,0.0002793973],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814599,0.008966349,0.0002093936,0.00352743,0.000006914399,0.0002487915,0.000001614644,0.000005814704,0.005573753],"genre_scores_gemma":[0.9426333,0.05595655,0.0007906391,0.0002397915,0.00002523155,0.00003437956,2.853153e-7,0.000008708911,0.0003110673],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8865593,"threshold_uncertainty_score":0.3912814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03137518567613678,"score_gpt":0.3660339583341216,"score_spread":0.3346587726579848,"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."}}