{"id":"W4401596164","doi":"10.1080/03612112.2024.2372203","title":"Pursuing Equity, Diversity, and Inclusion in Collection Development","year":2024,"lang":"en","type":"article","venue":"Dress","topic":"Library Science and Information Literacy","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Equity (law); Inclusion (mineral); Diversity (politics); Political science; Sociology; Gender studies; Law","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":[0.03279286,0.0003995651,0.0004590087,0.002727771,0.03832451,0.02216731,0.002335014,0.00208454,0.003597971],"category_scores_gemma":[0.01575284,0.0004512452,0.0004123804,0.002507931,0.0605671,0.009750316,0.03992893,0.004076163,0.0002349373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04149347,"about_ca_system_score_gemma":0.09705795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1707654,"about_ca_topic_score_gemma":0.3273964,"domain_scores_codex":[0.9709889,0.01811664,0.0005910669,0.001072125,0.003353501,0.0058778],"domain_scores_gemma":[0.9830155,0.00601283,0.001081845,0.001234025,0.002445329,0.006210526],"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":[0.00003399277,0.0001691468,0.02029516,0.0002264317,0.00002468689,0.0007538889,0.5684212,0.0003038439,0.0007765637,0.2845053,0.005548236,0.1189416],"study_design_scores_gemma":[0.00001631669,0.00009427047,0.01346345,0.0007637878,0.00002514271,0.0003376812,0.7067216,0.0002846692,0.001404422,0.08099255,0.1958428,0.00005335132],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.490183,0.003599961,0.02083302,0.07819118,0.0001849265,0.0004033738,0.00004257455,0.00009741554,0.4064645],"genre_scores_gemma":[0.9869943,0.0004723271,0.004068242,0.001518422,0.00001635345,0.00006712024,0.000007954242,0.00002097147,0.006834385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1707654,"threshold_uncertainty_score":0.3395427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04551060560937911,"score_gpt":0.3399674584868095,"score_spread":0.2944568528774304,"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."}}