{"id":"W2465482794","doi":"10.18438/b89d0p","title":"Educating Assessors: Preparing Librarians with Micro and Macro Skills","year":2016,"lang":"en","type":"article","venue":"Evidence Based Library and Information Practice","topic":"Library Science and Information Literacy","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Accreditation; Medical education; Information literacy; Professional development; Needs assessment; Psychology; Collection development; Skills management; Library science; Computer science; Sociology; Medicine; Pedagogy","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":["scholarly_communication"],"category_scores_codex":[0.0008127695,0.000136333,0.0001174243,0.0001873634,0.0006912861,0.001775868,0.0002559728,0.00007613403,0.0004164699],"category_scores_gemma":[0.001725321,0.00009106383,0.00002097233,0.0005264366,0.0002850738,0.7778692,0.00009637735,0.000119817,0.00006156715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001267763,"about_ca_system_score_gemma":0.0006888644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002406182,"about_ca_topic_score_gemma":9.168242e-8,"domain_scores_codex":[0.998522,0.0002550814,0.000399517,0.0001737117,0.0003584691,0.0002911834],"domain_scores_gemma":[0.9965546,0.002519385,0.0004169467,0.0002006438,0.00006617604,0.0002422322],"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":[0.0002687968,0.00006237609,0.01762576,0.0001247616,0.00002123797,0.000002293837,0.01370832,0.00001450823,0.0001316425,0.8216485,0.008336512,0.1380553],"study_design_scores_gemma":[0.0003232914,0.0001025256,0.0101347,0.0005941956,0.00001207787,0.00001808408,0.00533376,0.0001134778,0.001245666,0.0002515421,0.9816549,0.0002157273],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1215819,0.0007403386,0.00860827,0.6467745,0.000523804,0.001208565,0.00003632735,0.0007106619,0.2198156],"genre_scores_gemma":[0.5996823,0.005715365,0.07239257,0.3160866,0.0005186748,0.00009256758,0.00003752718,0.00002643936,0.00544794],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.9733185,"threshold_uncertainty_score":0.9992604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008473204708147998,"score_gpt":0.2744208291570531,"score_spread":0.2659476244489051,"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."}}