{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0002114875,0.0004564967,0.0004694695,0.0002836938,0.0002908185,0.993207,0.0007930848,0.00030527,0.00002989824],"category_scores_gemma":[0.0001485516,0.0004103619,0.0001365026,0.0004526206,0.0001204541,0.9832458,0.002828831,0.0003382509,0.000001803391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004759355,"about_ca_system_score_gemma":0.0008369403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000555281,"about_ca_topic_score_gemma":0.000007960895,"domain_scores_codex":[0.9974639,0.00004083949,0.0004536219,0.001176275,0.0004071091,0.0004582542],"domain_scores_gemma":[0.9981457,0.0006782556,0.0002087059,0.0005274746,0.00018968,0.0002501841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.000007147111,0.00002962068,0.000007038373,0.0001263777,0.00006494565,0.00001244798,0.001328546,0.00003064482,0.000003719657,0.001462461,0.0001479979,0.996779],"study_design_scores_gemma":[0.0004115995,0.00004597472,0.0003514189,0.003128246,0.00001134505,0.00002933237,0.9767963,0.003243167,0.0001442001,0.01046125,0.004642553,0.0007346603],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.749925,0.05164915,0.1820948,0.006101819,0.003295392,0.0009046249,1.519192e-7,0.0005598129,0.005469286],"genre_scores_gemma":[0.9637448,0.01129624,0.02351931,0.0007626206,0.0001997748,0.0001055622,0.0001072772,0.00003042442,0.0002339693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9960444,"threshold_uncertainty_score":0.9998348,"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."}}