{"id":"W4247889091","doi":"10.18665/sr.303501","title":"Finding a Way from the Margins to the Middle: Library Information Technology, Leadership, and Culture","year":2017,"lang":"en","type":"report","venue":"","topic":"Library Science and Information Literacy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Library science; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007103962,0.0002287486,0.0002453863,0.001685882,0.01289687,0.02635783,0.001024241,0.001400455,0.009487635],"category_scores_gemma":[0.01702967,0.0002899427,0.0001677716,0.002581163,0.01003226,0.01535987,0.008813879,0.004346858,0.001534643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005726433,"about_ca_system_score_gemma":0.01691807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0231964,"about_ca_topic_score_gemma":0.02849394,"domain_scores_codex":[0.9903843,0.005832205,0.0002742292,0.0003512871,0.001429317,0.001728623],"domain_scores_gemma":[0.9823537,0.00612605,0.001776054,0.0004742013,0.002415553,0.006854438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001708867,0.0005956095,0.06221557,0.0002374912,0.00003436605,0.0005873801,0.6042139,0.0001288688,0.0005172293,0.09216017,0.07784655,0.1612919],"study_design_scores_gemma":[0.00001006297,0.00005701764,0.02628282,0.0003328861,0.00001230729,0.0001412553,0.8840977,0.0001355628,0.0004064344,0.009525385,0.07895946,0.00003905018],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.5354099,0.003572126,0.001119831,0.1580979,0.0004967204,0.00005350085,0.0001077635,0.0001043235,0.301038],"genre_scores_gemma":[0.9878575,0.0008945393,0.0001950289,0.002509373,0.00006414747,0.00002174891,0.00001846415,0.00003710992,0.008402082],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02635783,"threshold_uncertainty_score":0.04612279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09863603079746286,"score_gpt":0.3080780523968822,"score_spread":0.2094420215994193,"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."}}