{"id":"W4413390690","doi":"10.18260/1-2--55513","title":"Biomedical Stakeholder Café – Continual Improvement & Integration of a Novel Adapted RADAR Framework for Stakeholders ","year":2025,"lang":"en","type":"article","venue":"","topic":"Biomedical and Engineering Education","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Stakeholder; Radar; Computer science; Business; Process management; Knowledge management; Telecommunications; 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":[0.01597512,0.000710302,0.0004387899,0.001770864,0.001880076,0.006903201,0.00229194,0.003006304,0.01230442],"category_scores_gemma":[0.01534599,0.0005167547,0.0008851869,0.001063156,0.002352644,0.007936722,0.007505266,0.003226609,0.003511742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001937623,"about_ca_system_score_gemma":0.005944661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003669766,"about_ca_topic_score_gemma":0.005195752,"domain_scores_codex":[0.9884053,0.006339015,0.0006424398,0.001288815,0.002335955,0.0009885366],"domain_scores_gemma":[0.986371,0.004794314,0.000974171,0.002928418,0.003185778,0.001746218],"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":[0.0002265267,0.001060644,0.004370302,0.0007823021,0.00008132377,0.001104619,0.01727889,0.009296488,0.01516648,0.6845884,0.03559902,0.230445],"study_design_scores_gemma":[0.0001074395,0.0004629467,0.003578066,0.0008397468,0.0001073896,0.001568735,0.01056415,0.1223786,0.01435779,0.1581083,0.6876491,0.0002776884],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01930944,0.0002052575,0.8916112,0.006728218,0.0002901559,0.000877213,0.0002610994,0.004795666,0.07592175],"genre_scores_gemma":[0.2802156,0.0001734827,0.6970617,0.001111313,0.0001143772,0.0005078343,0.0005190073,0.0005979809,0.01969864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01597512,"threshold_uncertainty_score":0.08448553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0666500974140596,"score_gpt":0.2792963623250289,"score_spread":0.2126462649109693,"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."}}