{"id":"W2056118104","doi":"10.1177/0160017605278998","title":"High-Poverty Nonmetropolitan Counties in America: Can Economic Development Help?","year":2005,"lang":"en","type":"article","venue":"International Regional Science Review","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Poverty; Census; Human capital; Basic needs; Economics; Development economics; Economic growth; Demographic economics; Population; Sociology; Demography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009029193,0.0001415591,0.0003229244,0.0006647067,0.000953034,0.002101711,0.0005494362,0.0009405319,0.002549566],"category_scores_gemma":[0.003060465,0.0001267811,0.0001563585,0.001311209,0.0007548997,0.001171628,0.0009179359,0.0006786363,0.000208006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001320365,"about_ca_system_score_gemma":0.003761321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03181719,"about_ca_topic_score_gemma":0.1220879,"domain_scores_codex":[0.9996191,0.0001821531,0.000008284006,0.00001709401,0.00003832068,0.0001348575],"domain_scores_gemma":[0.9984322,0.0004541267,0.0002464624,0.00002045279,0.0002676845,0.0005790631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002411016,0.0007403123,0.3339351,0.002010895,0.0002603347,0.001792524,0.007192746,0.002463935,0.0006015884,0.05515013,0.2376653,0.3579461],"study_design_scores_gemma":[0.000152763,0.0002661385,0.61714,0.002048221,0.0003404624,0.0006537174,0.07068302,0.002304827,0.0004017405,0.03164104,0.2743155,0.00005261679],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4690528,0.05609861,0.0005947142,0.435402,0.0006481399,0.00005121767,0.0005887822,0.00003810619,0.03752566],"genre_scores_gemma":[0.9454952,0.04279419,0.0006112489,0.006657406,0.0004500032,0.00002986771,0.0001987859,0.000006705519,0.003756453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03181719,"threshold_uncertainty_score":0.06326401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03182409665115099,"score_gpt":0.2591989301201458,"score_spread":0.2273748334689948,"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."}}