{"id":"W3004969498","doi":"10.3390/admsci10010007","title":"The Social Cost of Informal Electronic Waste Processing in Southern China","year":2020,"lang":"en","type":"article","venue":"Administrative Sciences","topic":"Recycling and Waste Management Techniques","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Social Innovation; Impact; University of British Columbia","funders":"","keywords":"China; Per capita; Informal sector; Per capita income; Estimation; Electronic waste; Economics; Agricultural economics; Business; Natural resource economics; Economic growth; Waste management; Environmental health; Population; Engineering; Geography; Medicine","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":[0.0009556168,0.0002475678,0.0001860685,0.00098853,0.0003656611,0.0006485222,0.0002947634,0.0002721803,0.001836912],"category_scores_gemma":[0.00286253,0.0001050864,0.0004897929,0.001260552,0.0006073609,0.0004980227,0.0008249183,0.0002492463,0.0000553954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004670836,"about_ca_system_score_gemma":0.001769213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06592605,"about_ca_topic_score_gemma":0.07203868,"domain_scores_codex":[0.9994792,0.0002320081,0.00002499749,0.00004287299,0.0001065262,0.000114364],"domain_scores_gemma":[0.9985846,0.0004998563,0.0005304841,0.00008317934,0.0002019248,0.0001000584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003140495,0.0001417989,0.7688374,0.0001720811,0.0003795344,0.001150536,0.0008050682,0.1793468,0.00123279,0.01193868,0.001474659,0.03420658],"study_design_scores_gemma":[0.00007289698,0.0002411939,0.8563951,0.00009016628,0.000278507,0.0003400384,0.002860414,0.1250599,0.001401087,0.007799399,0.005399856,0.00006146079],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971488,0.0001290355,0.0004181929,0.000250085,0.000002811338,0.00001225628,0.0002397729,0.000006680951,0.001792358],"genre_scores_gemma":[0.9993279,0.00006650307,0.0001131246,0.0000155312,0.000002076809,0.00000867551,0.0001113069,0.000001038664,0.000353947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06592605,"threshold_uncertainty_score":0.1310846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0303750689822704,"score_gpt":0.3117168156518383,"score_spread":0.2813417466695679,"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."}}