{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001921679,0.00005064017,0.00006834806,0.00006365433,0.001097589,0.00004702601,0.0001465117,0.00002336571,0.0003841473],"category_scores_gemma":[0.0001265723,0.00005107297,0.00002990404,0.0001350777,0.00007774598,0.0001337892,0.00003800782,0.0008307619,0.00001586141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006323535,"about_ca_system_score_gemma":0.002495804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009268012,"about_ca_topic_score_gemma":0.004511782,"domain_scores_codex":[0.9979971,0.0001993571,0.0001432791,0.00007898153,0.0004165169,0.001164737],"domain_scores_gemma":[0.9996325,0.0000807115,0.00007731987,0.00005547005,0.00003670965,0.0001172248],"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":[0.000007206908,0.00001913171,0.01882155,9.559806e-7,0.0000287208,0.000003270687,0.01703228,0.00002971032,0.0000268517,0.9407218,0.0008134387,0.02249504],"study_design_scores_gemma":[0.0002842396,0.00008344417,0.0008522522,0.000002048208,0.00001792828,0.0000636777,0.05542944,0.000009471663,0.000008475758,0.5008929,0.4422501,0.0001059877],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9764276,0.002425928,0.000273853,0.009703388,0.0007041364,0.0001274567,0.000002973871,0.00002670844,0.01030793],"genre_scores_gemma":[0.9921912,0.003975913,0.00003996655,0.0006670576,0.0006914769,0.000007652337,0.000002374089,0.000006159547,0.00241819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4414367,"threshold_uncertainty_score":0.8441875,"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."}}