{"id":"W2165199772","doi":"10.1080/0194262x.2014.906018","title":"Institutional Repository Literature: A Bibliometric Analysis","year":2014,"lang":"en","type":"article","venue":"Science & Technology Libraries","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Library science; Bibliometrics; Political science; Computer science","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":["metaresearch","bibliometrics","sts","scholarly_communication","open_science"],"consensus_categories":["bibliometrics","sts"],"category_scores_codex":[0.01882721,0.0002222887,0.0004908805,0.9268731,0.00229564,0.01096464,0.008373616,0.0003177473,0.0001700011],"category_scores_gemma":[0.07622401,0.0001523925,0.0002376394,0.9896259,0.007850381,0.004880622,0.002367905,0.0005473448,0.0002676498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001071688,"about_ca_system_score_gemma":0.000958069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000134197,"about_ca_topic_score_gemma":0.000002324511,"domain_scores_codex":[0.9862791,0.0001511618,0.0008175669,0.00159251,0.01011791,0.001041793],"domain_scores_gemma":[0.9913937,0.001897323,0.0003522581,0.002063547,0.003799362,0.0004937882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001099625,0.0001167112,0.4281031,0.000003197399,0.00005121543,0.00003434916,0.00009127338,0.0001725358,0.004581048,0.3815404,0.001935672,0.1833594],"study_design_scores_gemma":[0.0003292708,0.0002662938,0.5336683,0.00001191925,0.00004705872,0.00008879257,0.000201254,0.01666946,0.0294787,0.3530238,0.06577636,0.0004388261],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8809899,0.004757776,0.04497075,0.005798055,0.001366569,0.0002292985,0.0000226409,0.0004752577,0.06138976],"genre_scores_gemma":[0.9895456,0.00008582844,0.008011172,0.0001978552,0.00007706659,0.00001892534,0.000003656529,0.000006571251,0.002053355],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1829206,"threshold_uncertainty_score":0.9990032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1776202293842716,"score_gpt":0.4605730094745499,"score_spread":0.2829527800902784,"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."}}