{"id":"W4405540087","doi":"10.18438/eblip30554","title":"Evidence Synthesis Instructional Offerings in Library and Information Science Programs","year":2024,"lang":"en","type":"article","venue":"Evidence Based Library and Information Practice","topic":"Statistics Education and Methodologies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Library instruction; Library science; World Wide Web; Data science; Information retrieval; Information literacy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["metaresearch"],"domain":"methods","study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001479305,0.0001636217,0.0001580792,0.0007729377,0.0001831356,0.001851703,0.0002196648,0.00008403517,0.0002044204],"category_scores_gemma":[0.01953905,0.0001455592,0.00002410608,0.001315363,0.000305603,0.5415607,0.0001649008,0.000281113,0.00003415882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002025716,"about_ca_system_score_gemma":0.0007222422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002790262,"about_ca_topic_score_gemma":1.716048e-8,"domain_scores_codex":[0.998367,0.0002307214,0.0005912678,0.0001938482,0.0004007623,0.0002164287],"domain_scores_gemma":[0.9870453,0.01231897,0.0002446921,0.0002051567,0.00005690159,0.0001289826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000138462,0.000018065,0.0006977284,0.0008879953,0.000006385743,0.000002016439,0.0006755246,0.00000810548,0.00003205198,0.851501,0.0007619035,0.1452707],"study_design_scores_gemma":[0.0004453446,0.0003200796,0.0478064,0.009126765,0.0001432646,0.0003613504,0.006327058,0.02847472,0.009722635,0.03199805,0.8644031,0.0008712666],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.339848,0.01219991,0.1066184,0.4689873,0.006846943,0.005578336,0.0002493122,0.004489126,0.05518271],"genre_scores_gemma":[0.3141589,0.02252335,0.6436711,0.01883058,0.0001568135,0.0003593908,0.00005793725,0.00002847149,0.0002134589],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8636411,"threshold_uncertainty_score":0.9991845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08640414613821382,"score_gpt":0.3596981856044352,"score_spread":0.2732940394662213,"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."}}