{"id":"W3153523203","doi":"10.22148/001c.22335","title":"Introduction: Contemporary Culture After Data Science","year":2021,"lang":"en","type":"article","venue":"Journal of Cultural Analytics","topic":"Digital Humanities and Scholarship","field":"Arts and Humanities","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Affordance; Analytics; Sociology; Cultural analytics; Data science; Epistemology; Social science; Computer science; Human–computer interaction; World Wide Web; Philosophy; The Internet","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003659207,0.0001111733,0.0002105611,0.00006497731,0.0002688171,0.001602852,0.0005398634,0.00002369994,0.001958567],"category_scores_gemma":[0.0002054723,0.00006856412,0.0001074253,0.0001096481,0.000517439,0.00505863,0.0001409236,0.000284038,0.00003228462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004536046,"about_ca_system_score_gemma":0.0002353349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003547608,"about_ca_topic_score_gemma":0.00005423296,"domain_scores_codex":[0.99874,0.00002177713,0.0004029366,0.0001705955,0.0005045944,0.0001601096],"domain_scores_gemma":[0.9976045,0.00001318734,0.0002290468,0.0003144585,0.001725724,0.0001130144],"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":[0.0001210108,0.0002432858,0.0003399188,0.00005631271,0.0002490269,0.0007263847,0.0262366,0.0000192977,0.0004389888,0.2179541,0.7520463,0.00156876],"study_design_scores_gemma":[0.0001673279,0.00007784643,0.0001160616,0.00003675081,0.00004971862,0.0002285052,0.01914587,0.00002460111,0.0001839918,0.001183917,0.9786664,0.0001189706],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3641061,0.01036901,0.00002179335,0.01608113,0.006522454,0.0001047407,0.0002382855,0.00004525425,0.6025112],"genre_scores_gemma":[0.942575,0.000093661,0.0001098536,0.0005503737,0.006125568,2.240273e-7,0.00002964912,0.000005970356,0.05050968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5784689,"threshold_uncertainty_score":0.9994336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1232672934213013,"score_gpt":0.2890828548198972,"score_spread":0.1658155613985959,"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."}}