{"id":"W7117312558","doi":"10.5281/zenodo.18056544","title":"Barriers and pathways for advancing open science and open scholarship in academic institutions: a Canadian perspective","year":2025,"lang":"en","type":"preprint","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":"Concordia University","funders":"Fonds de recherche du Québec; Social Sciences and Humanities Research Council; Canada Research Chairs","keywords":"Scholarship; Corporate governance; Equity (law); Open science; Indigenous; Engaged scholarship; Work (physics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["open_science"],"domain":null,"study_design":"qualitative","genre":"empirical","about_ca_system":true,"about_ca_topic":true,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["open_science"],"domain":null,"study_design":"design_other","genre":"commentary","about_ca_system":true,"about_ca_topic":true,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication","open_science"],"consensus_categories":["scholarly_communication","open_science"],"category_scores_codex":[0.01081745,0.0002020822,0.0002459922,0.001517416,0.004315253,0.03821125,0.02061724,0.0001204273,0.00008030182],"category_scores_gemma":[0.02600012,0.0002305164,0.00001783995,0.001710755,0.0005578555,0.02619937,0.07946777,0.001168547,0.00003778956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00197237,"about_ca_system_score_gemma":0.001015843,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01633916,"about_ca_topic_score_gemma":0.001374807,"domain_scores_codex":[0.9963002,0.000557731,0.0003019088,0.001547401,0.0005754884,0.0007173003],"domain_scores_gemma":[0.9963894,0.0001167273,0.000166203,0.001199873,0.001401815,0.0007260148],"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":[0.00004424944,0.00002307587,0.00003643421,0.0001647602,0.00003261121,0.0000222782,0.00218641,0.0001230747,0.0001974808,0.9563168,0.005028748,0.03582403],"study_design_scores_gemma":[0.00107028,0.0001677447,0.001886146,0.0005176804,0.00001651375,0.00003476597,0.002866671,0.006922828,0.00009580771,0.0290004,0.9569517,0.0004694563],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01923884,0.002353228,0.1834604,0.07208792,0.0008791761,0.02771992,0.004352901,0.001063427,0.6888442],"genre_scores_gemma":[0.9456623,0.006056922,0.04163416,0.002753729,0.0001318539,0.0000121138,0.0008694563,0.0008266238,0.00205286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.951923,"threshold_uncertainty_score":0.996981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1866207023514491,"score_gpt":0.3937475157614601,"score_spread":0.207126813410011,"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."}}