{"id":"W4283209786","doi":"10.6017/ital.v41i2.15161","title":"Gathering Strength to Combat Access Inequality","year":2022,"lang":"en","type":"article","venue":"Information Technology and Libraries","topic":"Evaluation and Performance Assessment","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Public access; Order (exchange); Inequality; Public relations; Library science; Sociology; Political science; Computer science; Business; Mathematics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00120335,0.00007261663,0.0001255898,0.0008696103,0.0005763705,0.0004695129,0.0006371866,0.00004857429,0.002163881],"category_scores_gemma":[0.000473759,0.00005950748,0.00001785051,0.001491018,0.00008388413,0.003906142,0.000942038,0.0001822895,0.0001044687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000181367,"about_ca_system_score_gemma":0.00009230939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005645704,"about_ca_topic_score_gemma":0.000003613409,"domain_scores_codex":[0.9986764,0.00006526825,0.0004552474,0.000115092,0.0005590802,0.0001288751],"domain_scores_gemma":[0.9992454,0.0001716791,0.0001731861,0.0002861373,0.00008003758,0.00004354431],"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":[0.00004153467,0.00002261144,0.09316274,0.000006351707,0.000008786087,4.068055e-7,0.00349014,0.0009770218,0.00001780371,0.5353218,0.007783408,0.3591674],"study_design_scores_gemma":[0.0003435586,0.0001516097,0.04598166,0.000003412679,0.000002770935,0.000007152815,0.01228324,0.0041269,0.001332612,0.1402184,0.7953963,0.0001523399],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9622967,0.00005311688,0.008785934,0.01777966,0.0002726888,0.0002197596,0.00003573833,0.0002220212,0.01033436],"genre_scores_gemma":[0.9959838,0.000005651751,0.001022795,0.002576511,0.000009081979,0.00008769025,0.0000161268,0.000002143789,0.0002961505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7876129,"threshold_uncertainty_score":0.9987483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1181990340128073,"score_gpt":0.428156090342186,"score_spread":0.3099570563293787,"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."}}