{"id":"W2947186804","doi":"","title":"Leaving the Library: How We Improved Information Literacy by Joining Our User Communities","year":2019,"lang":"en","type":"article","venue":"Deep Blue (University of Michigan)","topic":"Library Science and Information Literacy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"H2020 Marie Skłodowska-Curie Actions","keywords":"Information literacy; Presentation (obstetrics); Work (physics); Literacy; Public relations; Value (mathematics); Process (computing); Library science; Space (punctuation); Sociology; Political science; Pedagogy; Engineering; Computer science; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002914298,0.00008607152,0.0001231718,0.0001261822,0.0008175987,0.0004031417,0.0008095253,0.00007050456,0.0003307123],"category_scores_gemma":[0.00001728951,0.00008038893,0.00007411487,0.0003848728,0.0001382059,0.04327281,0.0001821265,0.0001755744,0.0001629726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001285255,"about_ca_system_score_gemma":0.0001089815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004815222,"about_ca_topic_score_gemma":0.0004041738,"domain_scores_codex":[0.9992051,0.0001345778,0.0001221645,0.00006522246,0.0002537284,0.0002192553],"domain_scores_gemma":[0.9992804,0.00009839269,0.0002393263,0.0002396392,0.00007155153,0.00007071561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001577627,0.000010282,0.001161906,0.00002178378,0.00001299958,1.678639e-7,0.981895,0.00002047289,0.00008363998,0.007362029,0.003335783,0.006080146],"study_design_scores_gemma":[0.0001643572,0.00001868248,0.0004147822,0.00002336165,0.000003798195,2.932318e-7,0.5332164,0.002202655,0.00004027175,0.00005640633,0.463787,0.00007208668],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9642418,0.00006826218,0.0005263172,0.02415714,0.0002275237,0.0002200913,0.0000305536,0.00008448387,0.01044382],"genre_scores_gemma":[0.9863796,0.000194077,0.0009114873,0.002365031,0.00003786109,2.358329e-7,0.0000921984,0.000004341957,0.01001517],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4604512,"threshold_uncertainty_score":0.9701084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006733146820193625,"score_gpt":0.1952682844551419,"score_spread":0.1885351376349483,"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."}}