{"id":"W4400121582","doi":"10.2139/ssrn.4874676","title":"Inclusive Green Growth Dataset for African Countries","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Sustainability and Climate Change Governance","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Inclusive growth; Green growth; Geography; Development economics; Political science; Economic growth; Economics; Sustainable development; Poverty; Law","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":[0.0007721036,0.001035357,0.0007269626,0.009202533,0.0005332677,0.001377228,0.0007817612,0.000850639,0.02370186],"category_scores_gemma":[0.004808488,0.0003687629,0.0005904585,0.01650961,0.0002025442,0.0009790672,0.001588789,0.000991342,0.01358344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007763115,"about_ca_system_score_gemma":0.002220891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03079954,"about_ca_topic_score_gemma":0.0260949,"domain_scores_codex":[0.9994316,0.00008044692,0.00006381141,0.00009981473,0.0001316644,0.0001925988],"domain_scores_gemma":[0.9972621,0.0005510448,0.0007506032,0.0003448107,0.0007767636,0.0003145756],"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":[0.0001824426,0.00004800036,0.01230878,0.0008274268,0.00009007813,0.0001277701,0.0001552744,0.001606474,0.0001762849,0.002772207,0.967457,0.01424825],"study_design_scores_gemma":[0.0001295682,0.00002718657,0.06730065,0.0004139168,0.00005257915,0.0001538017,0.0006320323,0.0008317449,0.0007010065,0.001180473,0.9285461,0.00003083337],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003793424,0.0001830108,0.00009337506,0.0001326579,0.00002754537,0.00001489949,0.9934958,0.0001238272,0.002135452],"genre_scores_gemma":[0.01161534,0.0003298294,0.0004548418,0.00005168762,0.00002028321,0.0001757142,0.9844012,0.00009521816,0.002855946],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03079954,"threshold_uncertainty_score":0.07929057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006594235992930668,"score_gpt":0.2515456707631331,"score_spread":0.2449514347702024,"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."}}