{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007289419,0.0001086323,0.0002666935,0.0008726377,0.0002221756,0.0001021598,0.0003410667,0.0003106155,0.0005651544],"category_scores_gemma":[0.007942027,0.0001155673,0.00005511022,0.0004168108,0.0008550967,0.0001948015,0.0001531787,0.0004425634,0.0003036318],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001759839,"about_ca_system_score_gemma":0.008314145,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03476829,"about_ca_topic_score_gemma":0.0009046345,"domain_scores_codex":[0.996046,0.0002659835,0.0005788955,0.0003500457,0.002250588,0.0005084564],"domain_scores_gemma":[0.9936347,0.003555916,0.0001730364,0.0001647439,0.002296842,0.0001747591],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000007673826,0.00001303671,0.007863373,0.00005399562,0.00006584287,0.000004066805,0.000517608,0.000002875908,0.00001562937,0.9244853,0.06651422,0.0004563494],"study_design_scores_gemma":[0.0001918876,0.00005639364,0.005501169,0.0006407994,0.00001980907,0.000001759484,0.001661435,0.00005965459,0.00012734,0.8698401,0.1216646,0.0002350765],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0606428,0.0006157783,0.000002529256,0.01660582,0.002043399,0.0006942135,0.0003923344,0.00004007406,0.9189631],"genre_scores_gemma":[0.9562199,0.004566021,0.0001592133,0.0001874117,0.005254306,0.0001230228,0.0005730548,0.00003852059,0.03287854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8955771,"threshold_uncertainty_score":0.9973078,"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."}}