{"id":"W4232098727","doi":"10.32920/ryerson.14647431","title":"Assessing digital strategies and tools for legacy publishers: What works? What fails? And What's next?","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Library Collection Development and Digital Resources","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; York University","funders":"","keywords":"Dominance (genetics); Legacy system; Face (sociological concept); Context (archaeology); Digital library; Transition (genetics); World Wide Web; Computer science; Library science; Political science; Media studies; Sociology; Social science; History; Software; Art","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.04685275,0.001209704,0.001339165,0.01324878,0.00351341,0.03387392,0.002605594,0.002609722,0.003457737],"category_scores_gemma":[0.1107845,0.0004671301,0.0006410687,0.01184042,0.005857517,0.0276124,0.007524167,0.001765744,0.001300643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004733459,"about_ca_system_score_gemma":0.008121393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002458063,"about_ca_topic_score_gemma":0.006716118,"domain_scores_codex":[0.9660771,0.02020479,0.002696684,0.001387337,0.008028216,0.001605907],"domain_scores_gemma":[0.8356768,0.119285,0.01157256,0.006224066,0.02051739,0.006724009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001628192,0.0008509664,0.2456661,0.002438504,0.000236176,0.001002485,0.1603061,0.00070591,0.000584399,0.03731592,0.004390961,0.5463397],"study_design_scores_gemma":[0.00005388979,0.0008646091,0.08472411,0.002857165,0.0002777519,0.001032055,0.8061507,0.002825971,0.002896253,0.04236011,0.05577758,0.0001797835],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8707766,0.01283525,0.01759301,0.0119265,0.0002948192,0.001090921,0.0003023993,0.0002301051,0.08495043],"genre_scores_gemma":[0.9636808,0.006385515,0.02463848,0.0009227134,0.0001078605,0.0003072904,0.0001652855,0.00006941164,0.003722697],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9661261,"threshold_uncertainty_score":0.247784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0620581968227737,"score_gpt":0.2712999691427251,"score_spread":0.2092417723199514,"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."}}