{"id":"W2467848020","doi":"10.1108/jes-01-2015-0021","title":"Military spending, armed conflict and economic growth in developing countries in the post-Cold War era","year":2017,"lang":"en","type":"article","venue":"Journal of Economic Studies","topic":"Defense, Military, and Policy Studies","field":"Economics, Econometrics and Finance","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"Algoma University","funders":"University of Nottingham","keywords":"Economics; Fixed effects model; Ordinary least squares; Regression analysis; Econometrics; Random effects model; Post–Cold War era; Variables; Sample (material); Linear regression; Panel data; Cold war; Statistics; Mathematics; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002005465,0.0002604882,0.001042158,0.0004583311,0.0003903798,0.00007802369,0.0005499598,0.00007587662,0.00002591706],"category_scores_gemma":[0.0004374664,0.0002264611,0.000146934,0.00002925916,0.0004161506,0.0006297745,0.0001816102,0.0002762703,0.00009485957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005076667,"about_ca_system_score_gemma":0.00008014839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002409432,"about_ca_topic_score_gemma":0.007157196,"domain_scores_codex":[0.9978535,0.00003894325,0.001383841,0.0003057425,0.00003148231,0.0003864673],"domain_scores_gemma":[0.9982646,0.0004747052,0.0008698899,0.0002998254,0.00004150943,0.00004953933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009071687,0.000025018,0.8852326,0.00008451844,0.0004029403,0.00003989482,0.01514859,0.00004710578,7.262308e-7,0.09167264,0.007170061,0.00008516193],"study_design_scores_gemma":[0.001749912,0.0001588431,0.9187503,0.0001187295,0.0000206922,0.00005621822,0.005533363,0.00005675414,0.00002028882,0.01534791,0.0578338,0.0003531687],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9094493,0.07060298,0.000001310736,0.01330431,0.001130754,0.000187841,0.00007473086,0.000003926978,0.005244789],"genre_scores_gemma":[0.882796,0.1155293,0.0001586188,0.001003534,0.0003763261,0.00001266298,7.397202e-7,0.00001668516,0.0001061261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07632473,"threshold_uncertainty_score":0.9234815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07273245292007527,"score_gpt":0.3033041326838373,"score_spread":0.2305716797637621,"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."}}