{"id":"W4387578221","doi":"10.12927/hcpol.2023.27186","title":"Appendix 1: Macro Policy Data Collection Template","year":2023,"lang":"fr","type":"article","venue":"Healthcare policy","topic":"Regional Development and Policy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Macro; Computer science; Data collection; Data science; Statistics; Mathematics; Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.008271206,0.001276794,0.0015626,0.01147479,0.001718817,0.004551008,0.00230548,0.002336448,0.6394668],"category_scores_gemma":[0.07385936,0.002405131,0.001109648,0.01511235,0.0007950451,0.002550686,0.002068994,0.002869212,0.2956397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006191874,"about_ca_system_score_gemma":0.01508884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04172663,"about_ca_topic_score_gemma":0.04125334,"domain_scores_codex":[0.9944249,0.001656837,0.001331922,0.0004955631,0.001588733,0.0005018902],"domain_scores_gemma":[0.899596,0.06760345,0.004521626,0.006707495,0.01992946,0.001641963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006996801,0.00006612553,0.001019745,0.0005193805,0.00001036277,0.00003344103,0.0001326986,0.001069699,0.00004500879,0.00512847,0.98049,0.01141523],"study_design_scores_gemma":[0.0003260216,0.00003794031,0.005591697,0.0008803658,0.00002568722,0.00005281459,0.0008558041,0.0014358,0.0004394453,0.0139566,0.9763158,0.00008206902],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0003826239,0.00003290643,0.002215075,0.0004956683,0.000152143,0.002155121,0.9678366,0.0005261686,0.02620373],"genre_scores_gemma":[0.009162392,0.0005123133,0.03315806,0.001827416,0.0002674263,0.04359424,0.8364633,0.002425299,0.07258947],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.3605332,"threshold_uncertainty_score":0.5142568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1922828702717202,"score_gpt":0.459619453912032,"score_spread":0.2673365836403118,"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."}}