{"id":"W3129949684","doi":"10.5267/j.ac.2021.1.025","title":"Modeling broadband, mobile telephone and economic growth on a macro level: Empirical evidence from G7 countries","year":2021,"lang":"en","type":"article","venue":"Accounting","topic":"ICT Impact and Policies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cointegration; Ordinary least squares; Mobile telephone; Granger causality; Broadband; Information and Communications Technology; Mobile broadband; Economics; Econometrics; Empirical evidence; Causality (physics); Panel data; Macro; Mobile telephony; Telecommunications; Computer science; Mobile radio","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":[],"consensus_categories":[],"category_scores_codex":[0.001065557,0.0004740086,0.0004087723,0.001381508,0.0003347097,0.001654358,0.0003162753,0.0005777304,0.001766476],"category_scores_gemma":[0.002409227,0.0002097863,0.000906052,0.003226095,0.0005858891,0.0009626632,0.00115194,0.0008752987,0.000409837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005814961,"about_ca_system_score_gemma":0.001006745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0385151,"about_ca_topic_score_gemma":0.02676667,"domain_scores_codex":[0.9995711,0.0001810308,0.00002818242,0.00006726679,0.00004376182,0.0001085802],"domain_scores_gemma":[0.9977416,0.001272019,0.0004980711,0.0001287072,0.0002313169,0.0001282491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008207661,0.0001025858,0.9346559,0.0001131376,0.0003988513,0.0007179734,0.0005279486,0.04227726,0.0002795265,0.006389199,0.001838043,0.01261744],"study_design_scores_gemma":[0.00004370839,0.0003044407,0.81229,0.0003642465,0.001151029,0.0003020556,0.007158035,0.1527374,0.001275721,0.007274042,0.01700816,0.00009116947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940832,0.0009570023,0.001276741,0.0004002122,0.00001421385,0.000008708563,0.0008363127,0.00001667145,0.002406992],"genre_scores_gemma":[0.9963939,0.0008987325,0.0005459745,0.00004607499,0.00001663717,0.00001341111,0.00146204,0.000007590352,0.0006157089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0385151,"threshold_uncertainty_score":0.07658184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04028165979604197,"score_gpt":0.2759124610612094,"score_spread":0.2356308012651674,"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."}}