{"id":"W2081157080","doi":"10.1080/10598650.2009.11510636","title":"Building Diversity in Museums","year":2009,"lang":"en","type":"article","venue":"Journal of Museum Education","topic":"Museums and Cultural Heritage","field":"Arts and Humanities","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Diversity (politics); Museum education; Visual arts; Museology; Art; Sociology; Anthropology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001980616,0.00006859795,0.0001234101,0.0001423578,0.0001527191,0.0000782837,0.0001412425,0.00002528645,0.0004743837],"category_scores_gemma":[0.00002597595,0.0000534546,0.00007043744,0.00004367456,0.00002100365,0.0005108838,0.00001730235,0.0001474762,0.000008614252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008093435,"about_ca_system_score_gemma":0.00007217501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002178127,"about_ca_topic_score_gemma":0.0002353619,"domain_scores_codex":[0.9993965,0.00002429165,0.0002531724,0.00006165521,0.0001567032,0.0001076487],"domain_scores_gemma":[0.9995281,0.00001349362,0.0001936361,0.0000646032,0.0001457778,0.00005442839],"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":[0.0001388208,0.00190295,0.005837904,0.00005503638,0.00004348633,0.0000259816,0.1835942,0.00005062016,0.003694765,0.5675457,0.06457066,0.1725399],"study_design_scores_gemma":[0.001085325,0.0006560432,0.1574891,0.0004556551,0.00005858236,0.00006999588,0.03186525,0.00003049379,0.0003336619,0.02991934,0.7776,0.0004365477],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9795179,0.0001994417,0.00001161576,0.003735407,0.001335253,0.00004905627,8.247877e-7,0.000008140556,0.01514231],"genre_scores_gemma":[0.9977223,0.00004058254,0.0003353663,0.0005910549,0.0007638435,3.823306e-7,7.566086e-7,0.00000335588,0.0005423676],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7130294,"threshold_uncertainty_score":0.5194169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04218326732407243,"score_gpt":0.2712422407079758,"score_spread":0.2290589733839033,"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."}}