{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008103053,0.0004879794,0.0005325995,0.003335075,0.01133967,0.01184182,0.001382331,0.002235707,0.02284437],"category_scores_gemma":[0.02378931,0.0002782638,0.0003176368,0.002072137,0.01331687,0.009391268,0.02635954,0.003617568,0.001590291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007730712,"about_ca_system_score_gemma":0.02109949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0491502,"about_ca_topic_score_gemma":0.1016566,"domain_scores_codex":[0.9922866,0.002949351,0.000146231,0.0004027619,0.00173591,0.002479221],"domain_scores_gemma":[0.9870502,0.002628186,0.001024732,0.001081303,0.002222266,0.005993291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001736745,0.0003797178,0.0334511,0.0003041122,0.0001189152,0.0005615511,0.04019183,0.0008634804,0.001500694,0.613113,0.05693986,0.2524022],"study_design_scores_gemma":[0.000118847,0.000664958,0.03295179,0.001942548,0.0001070407,0.0003521902,0.1016055,0.001931663,0.001541464,0.2747604,0.5839425,0.00008098705],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2137864,0.002669601,0.0178383,0.1178204,0.0008285416,0.0002584048,0.0001007195,0.0002204627,0.6464772],"genre_scores_gemma":[0.960404,0.0007330792,0.002664603,0.007095189,0.0001633855,0.000110459,0.00003010665,0.00004656521,0.02875251],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0491502,"threshold_uncertainty_score":0.09772825,"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."}}