{"id":"W4313409859","doi":"10.22148/001c.57195","title":"From Concepts to Texts and Back: Operationalization as a Core Activity of Digital Humanities","year":2022,"lang":"en","type":"article","venue":"Journal of Cultural Analytics","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Operationalization; Realm; Epistemology; Process (computing); Construct (python library); Computer science; Sociology; Political science; Philosophy","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":[],"consensus_categories":[],"category_scores_codex":[0.0007466531,0.00006275346,0.0001891625,0.0001487424,0.0001798884,0.0005128946,0.0003884746,0.00001039387,0.0008962275],"category_scores_gemma":[0.0009333392,0.00004097034,0.00006868798,0.0004119571,0.00006388861,0.0005694751,0.0003578818,0.00008018909,0.00002190325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003972857,"about_ca_system_score_gemma":0.0000468719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003107542,"about_ca_topic_score_gemma":0.00001241762,"domain_scores_codex":[0.9980307,0.00005630786,0.000453379,0.0001576251,0.001229492,0.00007244764],"domain_scores_gemma":[0.99856,0.0002581641,0.0004156863,0.0001580464,0.0005471263,0.00006103314],"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":[0.0005259031,0.0006153151,0.02983414,0.00001870564,0.0004177375,0.00007687204,0.03405437,0.1509198,0.00689003,0.01165716,0.6797463,0.08524369],"study_design_scores_gemma":[0.0026412,0.002174649,0.06060961,0.0001338933,0.0002627749,0.0002412825,0.1367145,0.1049757,0.001803294,0.06195781,0.627681,0.000804256],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952267,0.00004888878,0.002299206,0.0008008137,0.0003667583,0.00004991862,0.0001073651,0.000002448328,0.001097842],"genre_scores_gemma":[0.9936478,0.000002698876,0.000359714,0.000144451,0.0001019906,2.67733e-7,0.00001294677,0.000001920886,0.005728169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1026602,"threshold_uncertainty_score":0.9813063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.203411329081511,"score_gpt":0.4160787014155996,"score_spread":0.2126673723340886,"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."}}