{"id":"W3206618455","doi":"10.1007/s40037-021-00685-6","title":"Barriers to cross-disciplinary knowledge flow: The case of medical education research","year":2021,"lang":"en","type":"article","venue":"Perspectives on Medical Education","topic":"Interdisciplinary Research and Collaboration","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; The Wilson Centre; Women's College Hospital; University of Toronto; University Health Network","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Medical education; Discipline; Cross disciplinary; Knowledge flow; Data science; Computer science; Medicine; Engineering ethics; Knowledge management; Sociology; Engineering; Social science","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1479075,0.0004532361,0.001243993,0.01812788,0.01836804,0.02935085,0.003509927,0.00674498,0.004848577],"category_scores_gemma":[0.3522387,0.0009569725,0.001006844,0.02004306,0.02017185,0.03139882,0.02061339,0.004159373,0.0005063385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01053031,"about_ca_system_score_gemma":0.02075032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005354251,"about_ca_topic_score_gemma":0.00376403,"domain_scores_codex":[0.8074146,0.1457376,0.01512191,0.007470219,0.01720207,0.00705365],"domain_scores_gemma":[0.3194066,0.6029603,0.03829269,0.01761036,0.0135293,0.008200704],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.000203802,0.0002286627,0.09839889,0.002011832,0.0001424168,0.004880534,0.5183741,0.0004639017,0.0006836377,0.2838375,0.002642851,0.08813184],"study_design_scores_gemma":[0.00009494749,0.0001747546,0.03646282,0.007371842,0.0001581272,0.005155942,0.66404,0.002122616,0.0008655193,0.2140989,0.06932021,0.0001341854],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8244022,0.01056893,0.02263097,0.07021496,0.0002370835,0.0005571046,0.0002026557,0.0000802684,0.0711059],"genre_scores_gemma":[0.9911186,0.001781073,0.005110511,0.001084473,0.0000856687,0.0001804418,0.00005453312,0.00002232109,0.0005624227],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8520924,"threshold_uncertainty_score":0.7822192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1004699010730668,"score_gpt":0.5765641964042865,"score_spread":0.4760942953312197,"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."}}