{"id":"W4280580375","doi":"10.14421/fhrs.2021.162.177-199","title":"ADOPSI DAN IMPLEMENTASI KECAKAPAN LITERASI INFORMASI DAN LITERASI DIGITAL UNTUK AKSELERASI UMKM DI INDONESIA PASCA PANDEMI COVID-19","year":2022,"lang":"en","type":"article","venue":"Fihris Jurnal Ilmu Perpustakaan dan Informasi","topic":"SMEs Development and Digital Marketing","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Quarter (Canadian coin); Small and medium-sized enterprises; Investment (military); Business; Economic growth; Political science; Public relations; Economics; Geography; Politics; Finance; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003119007,0.001125892,0.001082016,0.0009853382,0.005677139,0.003952888,0.002290331,0.0003642801,0.0004645],"category_scores_gemma":[0.001455658,0.001148942,0.000723166,0.002489906,0.0007514638,0.007297339,0.001711039,0.001714167,0.00003722029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002432686,"about_ca_system_score_gemma":0.003036044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002005718,"about_ca_topic_score_gemma":0.0002865404,"domain_scores_codex":[0.9900301,0.0005279399,0.002513775,0.0009890069,0.003145601,0.002793625],"domain_scores_gemma":[0.9943679,0.0008555928,0.0012803,0.0008647037,0.000478083,0.002153403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003892307,0.0002425374,0.8067355,0.0001491089,0.0002046108,0.0001664638,0.07333053,0.0000739853,0.00002220102,0.01061284,0.005238472,0.1028345],"study_design_scores_gemma":[0.002239002,0.0003240512,0.09300802,0.00007486529,0.00005550477,0.0002307095,0.05563153,0.0000977326,0.00002954378,0.0001566118,0.8468144,0.001338026],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8497998,0.0003362607,0.0001169609,0.00167529,0.001347929,0.001498304,0.00020475,0.0008075248,0.1442132],"genre_scores_gemma":[0.9834074,0.0002282491,0.0002470829,0.005453505,0.00081622,0.0003171231,0.001928548,0.0001313021,0.007470596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8415759,"threshold_uncertainty_score":0.999096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02780064387421467,"score_gpt":0.3016271882899357,"score_spread":0.273826544415721,"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."}}