{"id":"W7111631492","doi":"","title":"Mapping Canadian institutional research data management strategies: A cross-sectional study","year":2025,"lang":"","type":"preprint","venue":"OSF Preprints (OSF Preprints)","topic":"Research Data Management Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"RDM; Institutional research; Agency (philosophy); Incentive; Data management; Resource (disambiguation); Stewardship (theology); Research data","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01899208,0.0005743232,0.0007560066,0.007952648,0.01016219,0.00529576,0.003376451,0.0008547585,0.002687961],"category_scores_gemma":[0.04213144,0.00108503,0.0008433512,0.02235388,0.002752504,0.002174461,0.004493723,0.001424976,0.0004899993],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.08915386,"about_ca_system_score_gemma":0.1642987,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9841681,"about_ca_topic_score_gemma":0.989945,"domain_scores_codex":[0.9787372,0.003239754,0.002289587,0.002014572,0.01002454,0.003694284],"domain_scores_gemma":[0.939043,0.009051842,0.01163586,0.002927649,0.03263099,0.004710694],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007658035,0.0001044808,0.9335375,0.0004329903,0.0001067775,0.0001778064,0.03820155,0.0002053999,0.0003764627,0.001521382,0.003975058,0.02128398],"study_design_scores_gemma":[0.00000757678,0.00005068145,0.9149413,0.0002998658,0.00004766579,0.00009276695,0.06970599,0.0004534992,0.0003108955,0.0001125532,0.01391411,0.00006315505],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9801451,0.001112284,0.001080171,0.001541204,0.00002014704,0.0006587298,0.007495531,0.00003942955,0.007907525],"genre_scores_gemma":[0.9859708,0.001712935,0.004433121,0.0006878417,0.000008935865,0.0006447759,0.003790952,0.00004213139,0.002708473],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9810079,"threshold_uncertainty_score":0.6468596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2418520411332376,"score_gpt":0.4458045893479084,"score_spread":0.2039525482146708,"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."}}