{"id":"W4400100838","doi":"10.29173/iq1096","title":"Developing Institutional Research Data Management Strategies in Canada: Setting the Foundation for Stronger Partnerships and Collaborations","year":2024,"lang":"en","type":"article","venue":"IASSIST Quarterly","topic":"Research Data Management Practices","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Queen's University","funders":"","keywords":"RDM; Agency (philosophy); Alliance; Public relations; Stewardship (theology); Political science; Funding Agency; Government (linguistics); Public administration; Business; Sociology; Social science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","open_science"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2030992,0.0008670139,0.0009080232,0.008381468,0.04306928,0.04157535,0.01005241,0.007119743,0.006775313],"category_scores_gemma":[0.1908279,0.001876732,0.001134071,0.01434064,0.01915352,0.01630739,0.03585466,0.01247895,0.001180073],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.2867601,"about_ca_system_score_gemma":0.766301,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9298258,"about_ca_topic_score_gemma":0.9617252,"domain_scores_codex":[0.8262813,0.08084273,0.008372659,0.007168143,0.03770206,0.03963314],"domain_scores_gemma":[0.5709911,0.07765234,0.01859635,0.01954206,0.1269434,0.1862747],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002073385,0.0008141587,0.0417608,0.001739773,0.0001629372,0.001480724,0.1250149,0.00294612,0.002382055,0.2862118,0.2038993,0.33338],"study_design_scores_gemma":[0.0001699282,0.0001959283,0.03019569,0.002187074,0.00006439377,0.0001810176,0.2131501,0.003142083,0.001377682,0.04066936,0.7083952,0.0002714228],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.08800621,0.01075473,0.02489001,0.748451,0.00182053,0.003789982,0.0006994522,0.0008882587,0.1206998],"genre_scores_gemma":[0.7653434,0.009980682,0.1285577,0.055972,0.0004063323,0.002458057,0.0009409452,0.0003774725,0.03596331],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9899476,"threshold_uncertainty_score":0.9827206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3960048499086503,"score_gpt":0.4454829567131998,"score_spread":0.04947810680454956,"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."}}