{"id":"W4386037452","doi":"10.2139/ssrn.4545057","title":"“Library and Information Science” Explained and Embodied in 5 Minutes","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Library Science and Administration","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Embodied cognition; Data science; Computer science; Artificial intelligence","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":[0.001846426,0.001015998,0.0003936865,0.0008203881,0.005763136,0.004690487,0.0008889604,0.007246736,0.114101],"category_scores_gemma":[0.01459517,0.0005089658,0.0006776529,0.0006743473,0.00205507,0.00398436,0.004807381,0.007009552,0.0418411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004914639,"about_ca_system_score_gemma":0.003289629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01746355,"about_ca_topic_score_gemma":0.0327771,"domain_scores_codex":[0.9968503,0.001262704,0.0001566412,0.0002540901,0.0008108505,0.0006654721],"domain_scores_gemma":[0.9974664,0.001105373,0.0001169759,0.0001461618,0.0006475893,0.0005175038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004247932,0.00006138208,0.0005930102,0.0001682507,0.000009534068,0.0006058945,0.009703706,0.0001721899,0.00166777,0.08503594,0.8756304,0.02592716],"study_design_scores_gemma":[0.00001003361,0.00002946111,0.0004939372,0.00007185784,0.000002687105,0.00005386833,0.002074435,0.00008533322,0.0003731651,0.003306265,0.9934824,0.00001652837],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"commentary","genre_scores_codex":[0.01534969,0.00362093,0.01684296,0.2092939,0.05739033,0.000677886,0.003221373,0.002073971,0.6915289],"genre_scores_gemma":[0.09612474,0.000718476,0.004217691,0.04309862,0.00550621,0.0005456728,0.0008629494,0.0007740434,0.8481516],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.9953095,"threshold_uncertainty_score":0.3817058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01111768099313823,"score_gpt":0.2592572439160652,"score_spread":0.248139562922927,"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."}}