{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02512542,0.0006166088,0.0004238971,0.004036406,0.002316458,0.008918761,0.0006773598,0.001003828,0.004648769],"category_scores_gemma":[0.06485151,0.0003564532,0.0005222911,0.005914547,0.008048168,0.008242138,0.006339237,0.0012843,0.0007111236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004313638,"about_ca_system_score_gemma":0.008632132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003629601,"about_ca_topic_score_gemma":0.00273992,"domain_scores_codex":[0.976288,0.01302919,0.001720055,0.001982158,0.005250304,0.001730424],"domain_scores_gemma":[0.9266384,0.03646561,0.01984443,0.006461868,0.006136635,0.004453165],"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":[0.0001507586,0.0004430367,0.2195026,0.001121407,0.0001238991,0.0002795498,0.01269465,0.002424382,0.00114567,0.5780722,0.005967839,0.178074],"study_design_scores_gemma":[0.0001459094,0.0006950669,0.1859546,0.003718609,0.000299322,0.0006430483,0.05227033,0.008458315,0.006343004,0.5522889,0.1890263,0.0001566835],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5347298,0.006272693,0.08327179,0.02839288,0.0003062458,0.00183238,0.00226869,0.0005895451,0.3423359],"genre_scores_gemma":[0.9872152,0.0009636755,0.007859295,0.0004543944,0.00004355811,0.0004235353,0.0002772906,0.00003589679,0.002727169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02512542,"threshold_uncertainty_score":0.1328775,"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."}}