{"id":"W6950385372","doi":"10.5281/zenodo.3776976","title":"Across Canada, across Disciplines: Research Data Management Practices and Needs in the Social Sciences and Humanities","year":2017,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Research Data Management Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of British Columbia; McGill University; Queen's University; University of Toronto","funders":"","keywords":"RDM; Work (physics); Discipline; Session (web analytics); Archival science; Data management; Social research; Research ethics; Data management plan","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":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.03818288,0.0004441104,0.001030014,0.007231668,0.04160169,0.01989227,0.003175187,0.00186473,0.004611918],"category_scores_gemma":[0.08465216,0.001183862,0.0007107002,0.03240321,0.007022747,0.006717928,0.0107831,0.002948262,0.0006389573],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.2070957,"about_ca_system_score_gemma":0.5356953,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9951866,"about_ca_topic_score_gemma":0.9975486,"domain_scores_codex":[0.9427791,0.01213224,0.004147306,0.004231343,0.02648486,0.01022511],"domain_scores_gemma":[0.8415629,0.03683982,0.008273974,0.004991085,0.06427589,0.04405638],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0002417152,0.0001436086,0.1817474,0.002367903,0.0001418368,0.00146259,0.424009,0.0005034985,0.002160625,0.01665851,0.0687529,0.3018104],"study_design_scores_gemma":[0.00002558939,0.00006617208,0.2389268,0.001383877,0.00007158252,0.0002650069,0.5881293,0.00052559,0.0004981015,0.003021725,0.1668828,0.0002034049],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5975943,0.02230843,0.005651846,0.2867844,0.0007903065,0.001254521,0.007085533,0.0006664398,0.07786428],"genre_scores_gemma":[0.9162102,0.0154894,0.01501123,0.02120841,0.0001040621,0.0005447945,0.002478189,0.0002321924,0.02872151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9968248,"threshold_uncertainty_score":0.9196566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3913122645007103,"score_gpt":0.4545883290034828,"score_spread":0.06327606450277246,"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."}}