{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005150932,0.00007539471,0.00008555039,0.00001433443,0.000371832,0.00006876201,0.00041033,0.00002102468,0.00003961916],"category_scores_gemma":[0.00004166007,0.00004921907,0.00002772025,0.0004807382,0.0009014312,0.0002244754,0.000106788,0.0001095432,0.00001396177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001969849,"about_ca_system_score_gemma":0.00004387386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004763612,"about_ca_topic_score_gemma":0.0002031689,"domain_scores_codex":[0.9990517,0.00004192556,0.0001917867,0.0001693911,0.0002862679,0.0002588604],"domain_scores_gemma":[0.9997711,0.00002287462,0.0001317845,0.00004496129,0.000003809819,0.00002548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001949252,0.0001792941,0.1535986,0.00005991205,0.00001590601,0.000008217562,0.09240577,0.001959101,0.008566808,0.01125343,0.001644867,0.7301131],"study_design_scores_gemma":[0.001890444,0.006417004,0.4737136,0.0002276562,0.00005376383,0.000009220731,0.3172843,0.08995209,0.05881982,0.01371176,0.03610168,0.00181863],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9445169,0.00003112781,0.0003650749,0.002791878,0.00001378003,0.0002129878,0.000002079812,0.00002798086,0.05203822],"genre_scores_gemma":[0.9995016,0.00001117256,0.0001457372,0.0001130575,0.00001911325,0.00001199021,4.888534e-7,0.000002516892,0.0001943203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7282946,"threshold_uncertainty_score":0.3321361,"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."}}