{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002489029,0.0003261365,0.0003554403,0.001639749,0.01281465,0.009660754,0.001848913,0.001209981,0.01993268],"category_scores_gemma":[0.003049213,0.000368944,0.000505752,0.001469292,0.00794276,0.006450871,0.01940829,0.002283698,0.001117319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002776006,"about_ca_system_score_gemma":0.003585036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006657708,"about_ca_topic_score_gemma":0.02582546,"domain_scores_codex":[0.9980028,0.0008290338,0.00004840019,0.0001811755,0.000333852,0.0006046906],"domain_scores_gemma":[0.9978122,0.0003841916,0.0001407401,0.0004280657,0.0002195596,0.001015313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000187545,0.0003635886,0.03058634,0.0004350312,0.00009128894,0.001556975,0.1248343,0.003561604,0.002652397,0.5118445,0.03466591,0.2892206],"study_design_scores_gemma":[0.00004073745,0.0002555905,0.02561821,0.0006341376,0.00006565503,0.0007901597,0.1719021,0.0012621,0.001795109,0.1356699,0.6619112,0.00005511109],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3976163,0.002687846,0.007818028,0.01007738,0.0002753687,0.0001016884,0.00009800759,0.0001940754,0.5811313],"genre_scores_gemma":[0.9655506,0.0004327821,0.004215201,0.0002742195,0.00005581982,0.00002252279,0.0000379697,0.00004783789,0.02936305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01993268,"threshold_uncertainty_score":0.0666815,"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."}}