{"id":"W4376106853","doi":"10.1111/imcb.12653","title":"Equity, Diversity and Inclusion in Canadian immunology: communication and complexity","year":2023,"lang":"en","type":"article","venue":"Immunology and Cell Biology","topic":"Interdisciplinary Research and Collaboration","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôpital Maisonneuve-Rosemont; University of British Columbia; University of Toronto; Canadian Society for Immunology; Dalhousie University; University of Calgary; University of Victoria; Université de Montréal","funders":"Institute of Molecular and Cell Biology; Natural Sciences and Engineering Research Council of Canada","keywords":"Diversity (politics); Equity (law); Inclusion (mineral); Active listening; Public relations; Psychology; Political science; Engineering ethics; Knowledge management; Computer science; Engineering; Social psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.002530061,0.00009180175,0.0002048535,0.0005222158,0.003187343,0.00003669153,0.0005119095,0.0002405037,0.00006708421],"category_scores_gemma":[0.0002464982,0.00007687537,0.00001374233,0.0005150727,0.001134676,0.0001495104,0.03520049,0.0002635712,0.00003940431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005637579,"about_ca_system_score_gemma":0.00009086086,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04216972,"about_ca_topic_score_gemma":0.3564917,"domain_scores_codex":[0.9982963,0.0006566907,0.000229001,0.0003198121,0.0001067221,0.0003915249],"domain_scores_gemma":[0.9989553,0.0004967771,0.00006594232,0.0002890903,0.0001042582,0.00008864739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007557002,0.00008937234,0.7111034,0.00001489871,0.00006264609,0.00005690581,0.02583749,0.000003731125,0.06241031,0.07738091,0.00132777,0.1209569],"study_design_scores_gemma":[0.0007097151,0.0003422374,0.612816,0.000006396876,0.000003439346,0.0000156581,0.002747148,0.0008350876,0.0004477212,0.3758865,0.00606698,0.0001231763],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847872,0.002595028,0.000007962029,0.006955256,0.0001098604,0.0001383,0.00002523255,0.00001826161,0.005362959],"genre_scores_gemma":[0.9980438,0.001348004,0.00002809646,0.0001497779,0.000007855464,0.000006987314,0.0000445699,0.000002608628,0.0003683098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.314322,"threshold_uncertainty_score":0.9981104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1341768014961139,"score_gpt":0.4078712538607551,"score_spread":0.2736944523646412,"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."}}