{"id":"W7110631953","doi":"","title":"ANALYZING THE TRENDS OF GOVERNMENT SHARE OF HEALTH BEFORE, DURING, AND AFTER THE COVID-19 PANDEMIC","year":2025,"lang":"","type":"article","venue":"The Open Repository - Binghamton (Binghamton University)","topic":"Global Health Care Issues","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Public health; Typology; Accountability; Pandemic; Investment (military); Health care; Government spending","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.005767894,0.0007492204,0.001518792,0.0005056295,0.006782971,0.0001647659,0.003980613,0.0005232751,0.0003916961],"category_scores_gemma":[0.0003672825,0.0004799802,0.0004229734,0.002993156,0.001691952,0.0004907618,0.005404193,0.001847882,0.000009361146],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003824351,"about_ca_system_score_gemma":0.002881121,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01961662,"about_ca_topic_score_gemma":0.004850858,"domain_scores_codex":[0.9876519,0.006459103,0.002226826,0.001183286,0.001114252,0.001364569],"domain_scores_gemma":[0.9912429,0.001941926,0.003311756,0.0025655,0.0003932342,0.0005446878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00675977,0.0005664551,0.8765175,0.00692395,0.001561107,0.0001398626,0.06798965,0.0002149472,0.0004402588,0.01703606,0.01661216,0.005238212],"study_design_scores_gemma":[0.00636202,0.001147082,0.5762891,0.00670361,0.001484172,0.0001040572,0.09862573,0.0003145872,0.0004829834,0.0003852166,0.3071937,0.000907733],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9228861,0.01184491,0.0001019026,0.0203713,0.002101727,0.004803876,0.0005864289,0.00009776231,0.03720603],"genre_scores_gemma":[0.9284184,0.00183992,0.00003707661,0.001906684,0.0001226112,0.00002932829,0.00001237613,0.0000507228,0.06758286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3002284,"threshold_uncertainty_score":0.9999998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04888805418352409,"score_gpt":0.360687321951114,"score_spread":0.3117992677675899,"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."}}