{"id":"W4381327254","doi":"10.1080/15265161.2023.2207513","title":"Translational Justice in Human Gene Editing: Bringing End User Engagement and Policy Together","year":2023,"lang":"en","type":"letter","venue":"The American Journal of Bioethics","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Human Genome Research Institute","keywords":"Genome editing; Public engagement; Economic Justice; Corporate governance; Human genome; User engagement; Political science; Public policy; Sociology; Public relations; Genome; World Wide Web; Biology; Genetics; Gene; Computer science; Law; Management","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":[0.03252744,0.001292254,0.002852119,0.001252437,0.01518364,0.0194285,0.006764825,0.2426308,0.02329324],"category_scores_gemma":[0.1089423,0.002034742,0.002939538,0.001118555,0.01716981,0.01484857,0.01273485,0.1239135,0.01448074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01164103,"about_ca_system_score_gemma":0.03500396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01214238,"about_ca_topic_score_gemma":0.02689131,"domain_scores_codex":[0.9619927,0.00900855,0.003190943,0.004300771,0.01346505,0.008041958],"domain_scores_gemma":[0.8657671,0.0868366,0.004606375,0.00373309,0.01076461,0.02829224],"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":[0.00003954845,0.00007821451,0.0003067977,0.00004781426,0.00001963913,0.0007169305,0.0003233293,0.00006003867,0.0003696193,0.01436281,0.9736164,0.01005879],"study_design_scores_gemma":[0.0001645678,0.0001252369,0.001186941,0.0004142922,0.00004491424,0.001229091,0.001106824,0.0009958006,0.0005689994,0.06140166,0.9325969,0.0001648449],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0002086562,0.0003822587,0.0004754457,0.9886501,0.006156693,0.00001399828,0.00001775617,0.00007390558,0.004021224],"genre_scores_gemma":[0.002282936,0.0001737981,0.0004615234,0.9817125,0.007143822,0.00005198294,0.00001230608,0.00004349742,0.00811757],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.2426308,"threshold_uncertainty_score":0.1720237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03921967450502972,"score_gpt":0.3693222439803856,"score_spread":0.3301025694753559,"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."}}