{"id":"W2209710632","doi":"10.1111/jlme.12315","title":"Privacy and Biobanking in China: A Case of Policy in Transition","year":2015,"lang":"en","type":"article","venue":"The Journal of Law Medicine & Ethics","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Human Genome Research Institute; Chinese Academy of Medical Sciences; Chinese Universities Scientific Fund; Ministry of Science and Technology of the People's Republic of China; Chinese Academy of Sciences; Academy of Medical Sciences","keywords":"Biobank; China; Government (linguistics); Economic growth; Urbanization; Population; Epidemiological transition; Public health; Population ageing; Industrialisation; Business; Health care; Environmental health; Political science; Medicine; Economics; 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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01850885,0.0004801267,0.0007928617,0.001920058,0.02220693,0.01396043,0.002876248,0.01258205,0.003554587],"category_scores_gemma":[0.01618516,0.0005897774,0.00141114,0.004419962,0.02736119,0.00702586,0.0134565,0.01083149,0.0001883592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04852834,"about_ca_system_score_gemma":0.08455651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2546743,"about_ca_topic_score_gemma":0.1536165,"domain_scores_codex":[0.9815627,0.005427144,0.0006637871,0.001202409,0.002801148,0.008342721],"domain_scores_gemma":[0.9902997,0.004277939,0.0009929404,0.0007783168,0.0008527879,0.002798235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001443064,0.0001596842,0.01897634,0.00008929018,0.00005256784,0.01131554,0.05526328,0.001631415,0.0005841667,0.8927981,0.008253375,0.01073206],"study_design_scores_gemma":[0.000661749,0.0005144997,0.05180561,0.0009607294,0.0003040689,0.004098979,0.1517319,0.01911907,0.00242271,0.4931869,0.2744901,0.0007036976],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7259818,0.00287287,0.003050945,0.1813332,0.0004112676,0.0003238712,0.00009126202,0.00006853493,0.08586628],"genre_scores_gemma":[0.9702444,0.0005704046,0.0005021964,0.02203954,0.00008370507,0.0001310186,0.00002203417,0.00001143717,0.006395327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9874179,"threshold_uncertainty_score":0.506384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5892120725566127,"score_gpt":0.6150427715447462,"score_spread":0.0258306989881335,"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."}}