{"id":"W2591850731","doi":"","title":"Macroeconomic determinants of Emigration from Kenya","year":2016,"lang":"en","type":"preprint","venue":"MPRA Paper","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Emigration; Kenya; Attractiveness; Demographic economics; Inflation (cosmology); Economics; Destinations; Population; Per capita; Overtime; Exchange rate; Geography; Gravity model of trade; Development economics; Tourism; Labour economics; Demography; Political science; International economics; Macroeconomics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000257516,0.0001268323,0.0002371105,0.00004714719,0.00009973845,0.00005539336,0.0002898606,0.0003178309,0.002576918],"category_scores_gemma":[0.00009150369,0.0001090733,0.0001012739,0.00003567354,0.0001371014,0.0001236762,0.0001095246,0.0001355211,0.0001345554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009935485,"about_ca_system_score_gemma":0.0002843286,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.006870509,"about_ca_topic_score_gemma":0.1892102,"domain_scores_codex":[0.9989405,0.000131609,0.0003219099,0.0002706244,0.0001711725,0.0001642022],"domain_scores_gemma":[0.9991558,0.00008945169,0.0002947539,0.0003063223,0.00008543744,0.00006821365],"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":[0.0001421778,0.0002955454,0.6046739,0.0001865476,0.000268283,0.00001575803,0.1466752,0.0002276287,0.01498919,0.05651443,0.02981431,0.1461971],"study_design_scores_gemma":[0.001357254,0.00006622085,0.3151234,0.0007835144,0.0002016453,6.17754e-7,0.005908545,0.002158347,0.002533361,0.07001182,0.6001152,0.001740124],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814273,0.0001202095,0.0001545619,0.000640267,0.0009106493,0.0002216887,0.0003179249,0.00004412498,0.01616328],"genre_scores_gemma":[0.9929883,0.0005276856,0.0002580047,0.0002309277,0.0003890331,0.00002046812,0.00005073876,0.00001380527,0.005521009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5703009,"threshold_uncertainty_score":0.9997428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01640883250137004,"score_gpt":0.2994660812143147,"score_spread":0.2830572487129446,"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."}}