{"id":"W4205830211","doi":"10.18438/eblip29963","title":"Exploring Topics and Genres in Storytime Books: A Text Mining Approach","year":2021,"lang":"en","type":"article","venue":"Evidence Based Library and Information Practice","topic":"Digital Storytelling and Education","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Museum and Library Services","keywords":"Variety (cybernetics); Subject (documents); Computer science; Folklore; Creatures; Content analysis; Sentiment analysis; Library science; World Wide Web; Psychology; History; Artificial intelligence; Sociology; Social science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000479673,0.00008069938,0.0001012744,0.0001148074,0.0002511666,0.0001264148,0.00005143972,0.00006027327,0.00007833268],"category_scores_gemma":[0.001655589,0.00008023728,0.00001194902,0.0002188353,0.00002464837,0.1228459,0.000062653,0.0002686154,0.00003134208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002061104,"about_ca_system_score_gemma":0.0003711988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001169047,"about_ca_topic_score_gemma":1.138145e-7,"domain_scores_codex":[0.9989611,0.0002837688,0.0003447438,0.0001226429,0.0001221475,0.000165632],"domain_scores_gemma":[0.9980198,0.001539169,0.0001770872,0.0001320823,0.00004491187,0.00008691521],"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":[0.0009984979,0.0003351067,0.06408879,0.004253398,0.00005033076,0.00002425595,0.07611261,0.0008275819,0.00009919555,0.2154178,0.0164455,0.621347],"study_design_scores_gemma":[0.0003369797,0.00004384253,0.02854921,0.000867015,0.00001296217,0.000008829592,0.05768432,0.003097187,0.0001380944,0.00004111653,0.9090603,0.0001601499],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7906665,0.005158772,0.002531304,0.05610035,0.0008628677,0.0006315632,0.000008205187,0.000219122,0.1438213],"genre_scores_gemma":[0.8061764,0.01282823,0.05689562,0.1147185,0.0007944748,0.0005248708,0.0002119585,0.00003879302,0.0078112],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8926148,"threshold_uncertainty_score":0.8894224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1433786081079075,"score_gpt":0.3516829389437045,"score_spread":0.208304330835797,"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."}}