{"id":"W4313355726","doi":"10.2139/ssrn.4315888","title":"News Media, Inflation, and Sentiment","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Media Influence and Politics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bank of Canada","funders":"","keywords":"Inflation (cosmology); Economics; Sentiment analysis; Keynesian economics; Monetary economics; Econometrics; Computer science; Natural language processing; Physics; Theoretical physics","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":[0.0009581649,0.0001477815,0.0002156922,0.001554892,0.0005892214,0.003327559,0.0001585222,0.0007970363,0.009262932],"category_scores_gemma":[0.01042229,0.0001291818,0.0001833381,0.00235291,0.0005767104,0.00112972,0.0006324701,0.0007759816,0.001020206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004780919,"about_ca_system_score_gemma":0.0003749924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00391783,"about_ca_topic_score_gemma":0.00653454,"domain_scores_codex":[0.9994773,0.0002092611,0.00004418199,0.00004411008,0.0001364722,0.00008875014],"domain_scores_gemma":[0.990693,0.004048098,0.003296152,0.0001771985,0.0007955413,0.0009899704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007848873,0.0004355643,0.9547198,0.0001090644,0.0002049441,0.0002134502,0.001713542,0.0002936767,0.0004749087,0.006882109,0.007555912,0.02661211],"study_design_scores_gemma":[0.00002005772,0.00007889855,0.9903339,0.0000535979,0.00008635472,0.00006843224,0.001811726,0.0005144179,0.0001098218,0.002461422,0.004449121,0.00001233501],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9124223,0.002995908,0.0001433844,0.007649805,0.0002266526,0.00001250466,0.0008409195,0.00002025999,0.07568824],"genre_scores_gemma":[0.9964579,0.0008030407,0.00003140958,0.0002435096,0.0003673748,0.000003560732,0.0001755979,0.000007985704,0.001909491],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009262932,"threshold_uncertainty_score":0.03098756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01217273003309826,"score_gpt":0.2850985284837107,"score_spread":0.2729257984506124,"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."}}