{"id":"W4386214244","doi":"10.3386/w31619","title":"Political Sentiment and Innovation: Evidence from Patenters","year":2023,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Media Influence and Politics","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Politics; Sentiment analysis; Political science; Computer science; Natural language processing; Law","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":[],"consensus_categories":[],"category_scores_codex":[0.001623224,0.0001235396,0.0002119234,0.001511127,0.0005189847,0.001527309,0.0001875737,0.0006218046,0.007067599],"category_scores_gemma":[0.01053443,0.0001156503,0.0002222069,0.002040642,0.0004154453,0.0009777155,0.0005874621,0.0005514504,0.0009612957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003891638,"about_ca_system_score_gemma":0.0002754902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005598397,"about_ca_topic_score_gemma":0.006116398,"domain_scores_codex":[0.9994362,0.0001578073,0.00003980071,0.00009441358,0.0001547787,0.0001169872],"domain_scores_gemma":[0.9778689,0.008217018,0.01032302,0.000601507,0.001809988,0.001179551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003424637,0.0002933333,0.9597189,0.0001174679,0.0001188719,0.0001556295,0.001838502,0.00008963333,0.0008674215,0.001120555,0.003356546,0.03198063],"study_design_scores_gemma":[0.000017664,0.0001112287,0.9942919,0.00002741417,0.00004912014,0.00006983984,0.0009708978,0.0001411402,0.0003426509,0.0002563284,0.003715294,0.000006661943],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9799497,0.001353018,0.0001178248,0.0009433366,0.00002756409,0.00001753127,0.001025873,0.000004812338,0.01656045],"genre_scores_gemma":[0.9953928,0.001421984,0.0000698045,0.000250192,0.0001389711,0.00002012767,0.0005784229,0.000004518686,0.002123137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007067599,"threshold_uncertainty_score":0.02364343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7775655256727405,"score_gpt":0.639456068924053,"score_spread":0.1381094567486875,"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."}}