{"id":"W2934958963","doi":"10.1080/14494035.2019.1579505","title":"Designing stakeholder learning dialogues for effective global governance","year":2019,"lang":"en","type":"article","venue":"Policy and Society","topic":"Sustainability and Climate Change Governance","field":"Environmental Science","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Framing (construction); Compromise; Scholarship; Corporate governance; Political science; Institutionalisation; Process (computing); Global governance; Stakeholder; Social learning; Public relations; Sociology; Knowledge management; Computer science; Economics; Management","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":[0.06106966,0.001097575,0.0005643546,0.002342372,0.005828342,0.009854889,0.00334538,0.004760359,0.009739985],"category_scores_gemma":[0.06649782,0.000832089,0.0009640171,0.001341866,0.0118198,0.01413135,0.01614495,0.00368395,0.001527042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006379552,"about_ca_system_score_gemma":0.008766956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009506923,"about_ca_topic_score_gemma":0.001194458,"domain_scores_codex":[0.9282501,0.06325932,0.001513364,0.002307452,0.002554046,0.002115777],"domain_scores_gemma":[0.9347228,0.05190617,0.002680412,0.004975023,0.003194037,0.002521561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000249928,0.001269541,0.005913303,0.001099566,0.00007066519,0.0009769222,0.2059381,0.03342583,0.007674264,0.5660893,0.007130553,0.170162],"study_design_scores_gemma":[0.0001847742,0.0005152072,0.00133256,0.001408627,0.00005037204,0.0003131045,0.1225414,0.06147522,0.01029762,0.6649486,0.136799,0.0001335594],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1145527,0.000304301,0.776655,0.01648633,0.0001990976,0.002971689,0.0001382755,0.0008946753,0.08779792],"genre_scores_gemma":[0.6881509,0.0001928351,0.3050271,0.0006245403,0.00003009136,0.001929434,0.0001341912,0.000119502,0.003791406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06106966,"threshold_uncertainty_score":0.322971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02475651700103498,"score_gpt":0.2620685672840637,"score_spread":0.2373120502830288,"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."}}