{"id":"W4224451671","doi":"10.4018/978-1-7998-9702-6.ch009","title":"Building, Sustaining, and Growing Multidisciplinary, Multi-Departmental Partnerships to Teach Open Science Tools","year":2022,"lang":"en","type":"book-chapter","venue":"Advances in library and information science (ALIS) book series","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Multidisciplinary approach; Medical education; Engineering management; Service (business); Best practice; Engineering; Knowledge management; Computer science; Political science; Business; Medicine; Marketing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["open_science"],"domain":null,"study_design":"not_applicable","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["open_science"],"domain":null,"study_design":"design_other","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":["sts","scholarly_communication"],"category_scores_codex":[0.007734748,0.0004235594,0.0005123957,0.002193803,0.002661071,0.006654501,0.004913963,0.00007406206,0.0004186685],"category_scores_gemma":[0.001539602,0.0003626839,0.00005851575,0.001699788,0.003017109,0.7357208,0.01332938,0.0003833708,0.00003087136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001357818,"about_ca_system_score_gemma":0.0005424481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005095402,"about_ca_topic_score_gemma":0.00001025439,"domain_scores_codex":[0.993808,0.00006889676,0.001337481,0.001539919,0.002529849,0.0007158812],"domain_scores_gemma":[0.9969878,0.0005766606,0.0006853649,0.001160206,0.0001452097,0.0004448031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006432781,0.0000129206,0.0007325793,0.00001695167,0.000002648067,0.000007375827,0.002159832,0.0004598512,0.00001345975,0.9753493,0.0001326933,0.02104804],"study_design_scores_gemma":[0.0003621764,0.0002003349,0.001079889,0.0001283544,0.000005873563,0.0000273661,0.006034662,0.0006931417,0.0001819246,0.002948685,0.9878303,0.0005072349],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.005334571,0.001199492,0.0002627341,0.003182643,0.001574858,0.001669796,0.0002294897,0.000209114,0.9863373],"genre_scores_gemma":[0.3008055,0.2053072,0.219188,0.02097313,0.000820032,0.001120259,0.0008760652,0.0003934648,0.2505163],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9876977,"threshold_uncertainty_score":0.9998825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08664473437372724,"score_gpt":0.3766308814549332,"score_spread":0.289986147081206,"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."}}