{"id":"W4410631839","doi":"10.1080/13563467.2025.2504399","title":"Communication tools: a genealogy of quantitative easing","year":2025,"lang":"en","type":"article","venue":"New Political Economy","topic":"Media Studies and Communication","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Quantitative easing; Genealogy; Economics; Political science; Keynesian economics; History; Monetary policy; Central bank","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003437433,0.0009855218,0.000455107,0.006110186,0.003168324,0.007417249,0.001214452,0.001741559,0.009480079],"category_scores_gemma":[0.01212617,0.0005206453,0.00048263,0.002456133,0.02689025,0.01067902,0.003404016,0.002559933,0.001222955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004132635,"about_ca_system_score_gemma":0.002089454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003711909,"about_ca_topic_score_gemma":0.001681658,"domain_scores_codex":[0.9973099,0.00124138,0.0001307882,0.0006201926,0.0004851693,0.000212547],"domain_scores_gemma":[0.9908771,0.005592363,0.0008588657,0.00139958,0.0009757841,0.0002962984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003092588,0.00002335829,0.001036466,0.0001123034,0.00001496986,0.0001421527,0.005896905,0.001035662,0.0008217038,0.9475251,0.001389206,0.04197121],"study_design_scores_gemma":[0.00004047805,0.0001493361,0.003169157,0.0003810504,0.00005306723,0.0009356914,0.004786116,0.00666538,0.002842505,0.7612093,0.2196443,0.0001236362],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.07865751,0.01335047,0.3304984,0.01792792,0.000901852,0.0003837988,0.0005733239,0.0009858663,0.5567209],"genre_scores_gemma":[0.8305933,0.007414171,0.1177206,0.001638768,0.0006437879,0.00055426,0.0003055713,0.0004983507,0.04063114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009480079,"threshold_uncertainty_score":0.03171408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08868563376344478,"score_gpt":0.3956976920700144,"score_spread":0.3070120583065696,"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."}}