{"id":"W1523410580","doi":"10.17528/cifor/002255","title":"Capturing nested spheres of poverty: a model for multidimensional poverty analysis and monitoring","year":2007,"lang":"en","type":"book","venue":"Center for International Forestry Research (CIFOR) eBooks","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"FP7 International Cooperation; International Fund for Agricultural Development; Centre de Coopération Internationale en Recherche Agronomique pour le Développement; Sveriges Lantbruksuniversitet; Bundesministerium für Wirtschaftliche Zusammenarbeit und Entwicklung; European Commission; Overseas Development Institute; International Development Research Centre; Tinker Foundation; Nature Conservancy; Institut Alam Sekitar dan Pembangunan, Universiti Kebangsaan Malaysia; International Tropical Timber Organization; Margot Marsh Biodiversity Foundation; United Nations Educational, Scientific and Cultural Organization; John D. and Catherine T. MacArthur Foundation","keywords":"Poverty; SPHERES; Economics; Economic growth; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004532582,0.001040079,0.0009229699,0.002029778,0.001196095,0.003437575,0.00228003,0.001376738,0.003520871],"category_scores_gemma":[0.01346581,0.0005886688,0.001875666,0.003009254,0.003363063,0.006540498,0.00407404,0.002499011,0.0004455116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003250811,"about_ca_system_score_gemma":0.002000829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009929393,"about_ca_topic_score_gemma":0.008614982,"domain_scores_codex":[0.9960376,0.002632706,0.0001461336,0.0005071965,0.0004388207,0.0002375052],"domain_scores_gemma":[0.9933426,0.004708505,0.0006887664,0.0004621234,0.0005156752,0.0002822847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001005325,0.0001718999,0.0156084,0.0001905003,0.0001606189,0.0003204202,0.004681219,0.2078614,0.0004002034,0.7110437,0.003613179,0.05584792],"study_design_scores_gemma":[0.00002097462,0.00005098848,0.002211922,0.00007341536,0.0000361202,0.0001405711,0.001195252,0.5511993,0.00009857705,0.4394436,0.005484618,0.00004465757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02670831,0.0003797266,0.9597059,0.002451836,0.00004865639,0.0001927972,0.0003894656,0.000164784,0.009958497],"genre_scores_gemma":[0.4572003,0.000603855,0.5365379,0.0002613767,0.00006324101,0.001275697,0.0004395967,0.00007820858,0.00353971],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009929393,"threshold_uncertainty_score":0.02397084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1259017691383189,"score_gpt":0.4155659475413275,"score_spread":0.2896641784030086,"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."}}