{"id":"W4392453320","doi":"10.32920/25343236.v1","title":"Weaving Open Dialogue Using Canada’s Open Science Roadmap Framework","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Interdisciplinary Research and Collaboration","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Weaving; Open science; Computer science; Political science; Engineering; Physics; Mechanical engineering","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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.04950638,0.001521559,0.000951804,0.009013019,0.04097541,0.04366648,0.00632113,0.01077932,0.01765136],"category_scores_gemma":[0.04800714,0.001663871,0.002072227,0.009301797,0.051052,0.0208417,0.03811826,0.01099002,0.003330175],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1120623,"about_ca_system_score_gemma":0.2460505,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.808829,"about_ca_topic_score_gemma":0.8618909,"domain_scores_codex":[0.9317952,0.04046838,0.002098584,0.003834979,0.01467527,0.007127576],"domain_scores_gemma":[0.9343945,0.03214189,0.001347409,0.006263889,0.01495694,0.01089539],"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":[0.00002143029,0.00003001033,0.0005232202,0.0002559119,0.00001675331,0.0004729145,0.05718989,0.001434603,0.0002962778,0.8858854,0.02804804,0.02582561],"study_design_scores_gemma":[0.00002183014,0.00001552946,0.0004881479,0.000868523,0.00001833507,0.0001705094,0.03863975,0.002670438,0.0003376592,0.2140456,0.7426096,0.0001141144],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01103606,0.004071564,0.3256215,0.09963179,0.002218409,0.001433425,0.001082123,0.001354039,0.5535511],"genre_scores_gemma":[0.4627584,0.004885284,0.405349,0.01539447,0.0005382267,0.003041484,0.001467946,0.001644133,0.1049209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9936789,"threshold_uncertainty_score":0.813073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2682091005366194,"score_gpt":0.5323581003030471,"score_spread":0.2641489997664276,"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."}}