{"id":"W4362565039","doi":"10.1007/978-3-031-14197-3_8","title":"Using Natural Language Processing to Measure COVID-19-Induced Economic Policy Uncertainty for Canada and the USA","year":2023,"lang":"en","type":"book-chapter","venue":"Contributions to statistics","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Index (typography); Coronavirus disease 2019 (COVID-19); Computer science; Artificial intelligence; Contraction (grammar); Natural language processing; Econometrics; Political science; Economics; Linguistics; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009375711,0.0002975924,0.0006692789,0.000235191,0.0005405207,0.0001751736,0.000252004,0.0001633048,0.00007823206],"category_scores_gemma":[0.003853402,0.000296342,0.00008417747,0.00008798046,0.00008714038,0.00003877305,0.0001352585,0.0002682117,0.00001843609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002714637,"about_ca_system_score_gemma":0.002229766,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.429212,"about_ca_topic_score_gemma":0.8156056,"domain_scores_codex":[0.998309,0.00001849212,0.0006783986,0.0005225145,0.00006210392,0.0004094847],"domain_scores_gemma":[0.9980106,0.0006962751,0.0003862187,0.0003715477,0.0001923983,0.0003429583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001065697,0.000003354352,0.0002385605,0.0001007849,0.00009700013,0.000005282579,0.000192657,0.0005156054,0.000001119189,0.9912581,0.004405871,0.003075074],"study_design_scores_gemma":[0.002294948,0.00005658959,0.0008346546,0.00009215087,0.0001187257,0.00001024026,0.00005953834,0.5399191,8.369794e-7,0.2242739,0.231295,0.001044354],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002769735,0.001746873,0.5672215,0.01062599,0.001950089,0.005057495,0.3961369,0.00009580969,0.01439556],"genre_scores_gemma":[0.8944929,0.00009342848,0.005099294,0.005127123,0.001068425,0.0002512956,0.001903495,0.0001842614,0.09177978],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8917232,"threshold_uncertainty_score":0.9999489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05082941127230059,"score_gpt":0.3147193364294058,"score_spread":0.2638899251571052,"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."}}