{"id":"W2993740281","doi":"10.22148/16.035","title":"Data Cultures, Culture as Data – Special Issue of Cultural Analytics","year":2019,"lang":"en","type":"article","venue":"Journal of Cultural Analytics","topic":"Digital Humanities and Scholarship","field":"Arts and Humanities","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Research data; Data science; Computer science; Sociology; Data curation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006010638,0.0004208313,0.0009077869,0.000144907,0.0002505353,0.00134818,0.003354106,0.0001510015,0.007021094],"category_scores_gemma":[0.0003482439,0.0002509706,0.0003377084,0.0001318396,0.0004117999,0.005368162,0.0008185334,0.0007932212,0.0003070674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008156996,"about_ca_system_score_gemma":0.0001378887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009359731,"about_ca_topic_score_gemma":0.0006624048,"domain_scores_codex":[0.9965551,0.0000759668,0.001339412,0.0004193672,0.00117207,0.0004380445],"domain_scores_gemma":[0.9955323,0.00007456904,0.001251732,0.001302845,0.001596931,0.0002415953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001875331,0.0002718547,0.000312419,0.0001427322,0.0008289012,0.00008504861,0.02056751,0.0001374556,0.0001330837,0.04817985,0.927821,0.001332556],"study_design_scores_gemma":[0.0007198695,0.0004243542,0.00007050575,0.0001693668,0.0004812406,0.0001393997,0.04780245,0.0004448002,0.00005871513,0.001368513,0.9479311,0.0003896549],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2525089,0.003096891,0.00001185724,0.002564549,0.006294766,0.0005178056,0.004866995,0.00006479594,0.7300735],"genre_scores_gemma":[0.7220341,0.0009514285,0.0004421735,0.000978183,0.02466464,4.972925e-7,0.002137095,0.00005475561,0.2487371],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.4813363,"threshold_uncertainty_score":0.9999943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1263592046849277,"score_gpt":0.3235582065088711,"score_spread":0.1971990018239434,"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."}}