{"id":"W3168160492","doi":"10.1016/j.esg.2021.100109","title":"‘Power-sensitive design principles’ for climate change adaptation policy-making in South Asia","year":2021,"lang":"en","type":"article","venue":"Earth System Governance","topic":"Sustainability and Climate Change Governance","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre; Department for International Development; Department for International Development, UK Government; Government of the United Kingdom","keywords":"Panacea (medicine); Context (archaeology); Adaptation (eye); Power (physics); Climate change; Political science; Climate change adaptation; Development economics; Psychology; Economics; Geography; Medicine; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000677389,0.000227412,0.0003089815,0.00002348184,0.0001678899,0.00005842607,0.0001832888,0.0001123238,0.00009200011],"category_scores_gemma":[0.0004345896,0.0002437228,0.00009913059,0.0005200884,0.00007827799,0.0004074089,0.0001843879,0.000123724,0.0001491974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009275652,"about_ca_system_score_gemma":0.0000761201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002132628,"about_ca_topic_score_gemma":0.0009537801,"domain_scores_codex":[0.9976677,0.0001916897,0.0004071654,0.0006193742,0.0004398948,0.0006741197],"domain_scores_gemma":[0.9989082,0.0001583916,0.0004204478,0.0003868854,0.00004929076,0.00007684296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.002474635,0.001057488,0.1671395,0.00529473,0.0001341028,0.002176033,0.4906304,0.09748469,0.006438014,0.1964249,0.0007339047,0.03001158],"study_design_scores_gemma":[0.002470196,0.0002874262,0.83343,0.00186072,0.00002821361,0.0001047446,0.06962644,0.06032189,0.005827023,0.0002694518,0.02461609,0.001157807],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.951737,0.0007905673,0.01976812,0.001534594,0.000754338,0.003343715,0.0006269701,0.0002146898,0.02123],"genre_scores_gemma":[0.9958599,0.00005722215,0.002901579,0.0002642813,0.0001710566,0.0003006166,0.000007399115,0.00003182047,0.0004060868],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6662905,"threshold_uncertainty_score":0.9938726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04843627922986019,"score_gpt":0.2607295860227549,"score_spread":0.2122933067928947,"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."}}