{"id":"W2908882000","doi":"10.3386/w25429","title":"How Does Scientific Progress Affect Cultural Changes? A Digital Text Analysis","year":2019,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Affect (linguistics); Data science; Computer science; Psychology; Communication","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01316522,0.0002475851,0.0008155692,0.002826831,0.0007371088,0.00239668,0.0009694604,0.0003700764,0.0007149416],"category_scores_gemma":[0.003174611,0.0001894263,0.000746133,0.002277061,0.001729558,0.0006173794,0.0002704871,0.0005281755,0.0001382392],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002027285,"about_ca_system_score_gemma":0.006089145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001795232,"about_ca_topic_score_gemma":0.007074442,"domain_scores_codex":[0.9925542,0.0006926091,0.0004706406,0.0009238444,0.004757517,0.0006011119],"domain_scores_gemma":[0.990873,0.002201708,0.0005661484,0.0003430198,0.005821106,0.0001949995],"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":[0.0001648163,0.0007662187,0.08480556,0.0008073617,0.0154666,0.00002138984,0.005622695,0.001550673,0.00008503025,0.5506291,0.1547208,0.1853597],"study_design_scores_gemma":[0.0005119202,0.0001430104,0.007620294,0.0001684004,0.0006808651,0.000004159853,0.00300493,0.00264972,0.00007942901,0.172278,0.811914,0.0009452188],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03678822,0.002640478,0.00007919392,0.01484272,0.003020999,0.002124182,0.0009023479,0.00008914342,0.9395127],"genre_scores_gemma":[0.815609,0.0002266657,0.000259996,0.000004565827,0.001542658,0.00008348507,0.001504949,0.00001979444,0.1807489],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7788208,"threshold_uncertainty_score":0.9995454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4194674362147799,"score_gpt":0.5880463222317006,"score_spread":0.1685788860169207,"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."}}