{"id":"W6930662980","doi":"10.5281/zenodo.15428051","title":"The Institutional Alignment Challenge: Grappling with AI in Collections","year":2025,"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":"Ontario Council of University Libraries; University of Toronto","funders":"","keywords":"Outreach; Presentation (obstetrics); Intellectual property; Openness to experience; Diversity (politics); Work (physics); Generative grammar","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.09957117,0.0006393678,0.001085977,0.004837752,0.03642543,0.06975104,0.007609667,0.01172011,0.01502149],"category_scores_gemma":[0.1238042,0.001151187,0.0009864245,0.009943795,0.0558462,0.0528826,0.05721481,0.01890632,0.003923373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01846874,"about_ca_system_score_gemma":0.0461571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01184598,"about_ca_topic_score_gemma":0.01432543,"domain_scores_codex":[0.8312603,0.1275348,0.004877531,0.007499547,0.01974962,0.009078339],"domain_scores_gemma":[0.8432713,0.08868414,0.00844219,0.02287992,0.01467308,0.02204943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009785274,0.0001685641,0.004716869,0.0009360997,0.00006659752,0.001040829,0.2359953,0.001604263,0.0009860032,0.5226005,0.06984519,0.1619419],"study_design_scores_gemma":[0.00002163059,0.00005124418,0.000963898,0.0006864581,0.00002063545,0.0006397095,0.1881545,0.001137872,0.0008994633,0.2225928,0.5847383,0.00009336074],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.05041196,0.009786755,0.127956,0.5466483,0.004450017,0.0004351395,0.0002347888,0.001406478,0.2586706],"genre_scores_gemma":[0.7639528,0.00740425,0.1108206,0.0584695,0.003580127,0.0009037513,0.0004836537,0.002176313,0.05220902],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.930249,"threshold_uncertainty_score":0.526589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04808661605188742,"score_gpt":0.2941847718819861,"score_spread":0.2460981558300987,"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."}}