{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.002444127,0.0002502416,0.0002272649,0.007046995,0.0008945543,0.003144681,0.0004141884,0.0004627315,0.003896033],"category_scores_gemma":[0.02588585,0.0001389388,0.0003038169,0.009751453,0.001327761,0.003976139,0.001272856,0.0006738144,0.0008673655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001317197,"about_ca_system_score_gemma":0.0006873731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003813831,"about_ca_topic_score_gemma":0.003488482,"domain_scores_codex":[0.9977188,0.001012499,0.0002340329,0.0003773709,0.0005498826,0.0001074502],"domain_scores_gemma":[0.9573822,0.03185807,0.005851466,0.001400891,0.003058372,0.0004488976],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006308178,0.000573573,0.5448676,0.001719527,0.0002804741,0.001018358,0.05401244,0.002367026,0.01214482,0.0219501,0.01395687,0.3464783],"study_design_scores_gemma":[0.00006201059,0.0002764765,0.8067077,0.0004949999,0.000252984,0.0005953632,0.06478276,0.02781194,0.01100297,0.01911038,0.06876972,0.000132574],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9740756,0.0008866157,0.004472351,0.002363909,0.00008322004,0.0001417458,0.007081804,0.00008305338,0.01081166],"genre_scores_gemma":[0.986226,0.0006954557,0.006877009,0.0002079768,0.0001593048,0.0001953407,0.003779377,0.00004047204,0.001818958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9975559,"threshold_uncertainty_score":0.01303351,"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."}}