{"id":"W3159343556","doi":"10.17762/turcomat.v12i8.3029","title":"Covid-19 in indonesia: Socio-economic impact and policy response","year":2021,"lang":"tr","type":"article","venue":"Türk bilgisayar ve matematik eğitimi dergisi","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Tourism; Unemployment; Coronavirus disease 2019 (COVID-19); Indonesian; Poverty; Pandemic; Poverty rate; Economic impact analysis; Demographic economics; Business; Economics; Development economics; Geography; Economic growth; Socioeconomics; Infectious disease (medical specialty); Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001689256,0.0003579011,0.0003671049,0.0009378234,0.001352332,0.003346126,0.0009319342,0.002880332,0.008646425],"category_scores_gemma":[0.003300825,0.0002004102,0.0006907952,0.0008680054,0.0009559123,0.001694003,0.0032949,0.0039861,0.0007913456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004866706,"about_ca_system_score_gemma":0.0112846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04740827,"about_ca_topic_score_gemma":0.04845407,"domain_scores_codex":[0.9988025,0.0002921197,0.00005657044,0.00005597038,0.0001635774,0.0006291899],"domain_scores_gemma":[0.9972458,0.0002824574,0.0004841163,0.00003320858,0.000459525,0.001495001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000766463,0.001947588,0.328728,0.004190685,0.000314268,0.006351361,0.00546351,0.004772401,0.004010286,0.08000079,0.3499424,0.2135123],"study_design_scores_gemma":[0.0001272255,0.000602459,0.6097924,0.004191685,0.0001341104,0.001138043,0.03207491,0.004943347,0.001396584,0.01571028,0.3297567,0.0001322487],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2639144,0.0320499,0.0008323544,0.5942174,0.006356644,0.0002946742,0.005412789,0.0001748705,0.096747],"genre_scores_gemma":[0.8655121,0.0265238,0.001471443,0.08379319,0.001420114,0.0002161504,0.002117165,0.00005945367,0.01888652],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04740827,"threshold_uncertainty_score":0.09426469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04345942593929809,"score_gpt":0.3251059485161911,"score_spread":0.281646522576893,"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."}}