{"id":"W4399828482","doi":"10.32920/26060791.v1","title":"Data Governance. Enablers, Inhibitors, Practices, and Outcomes","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Corporate governance; Business; Data governance; Knowledge management; Process management; Computer science; Finance; Marketing; Data quality","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00989279,0.000315001,0.0005585805,0.0001590198,0.00009304339,0.003368906,0.004007644,0.0002052838,0.001221853],"category_scores_gemma":[0.01121794,0.0002045337,0.00008691324,0.000296064,0.0001286094,0.0009327091,0.05069949,0.000770708,0.00171289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004777802,"about_ca_system_score_gemma":0.0002125767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002140286,"about_ca_topic_score_gemma":0.002289539,"domain_scores_codex":[0.9942287,0.0002649825,0.000979422,0.002067877,0.00217865,0.0002804049],"domain_scores_gemma":[0.992197,0.001380744,0.001163917,0.005018185,0.00009873846,0.0001414174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006270691,0.00004020054,0.0008242495,0.0001264761,0.0001102897,0.00003293786,0.00009843209,0.000005079965,9.610499e-7,0.0526919,0.8962207,0.04984244],"study_design_scores_gemma":[0.00009142136,0.000008640599,0.00129881,0.00007074012,0.00008338413,0.000002383014,0.0006704703,0.001126235,0.000005157995,0.1340066,0.8623864,0.0002497231],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01335229,0.02032716,0.005037674,0.3035699,0.02747725,0.00298212,0.02491206,0.0009604923,0.601381],"genre_scores_gemma":[0.2336483,0.01276318,0.03549234,0.01438888,0.002452796,0.0001481651,0.004426539,0.0001662104,0.6965136],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2891811,"threshold_uncertainty_score":0.9996912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3969939616042326,"score_gpt":0.4907735664996202,"score_spread":0.09377960489538761,"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."}}