{"id":"W2121884952","doi":"10.1111/ecoj.12099","title":"Survival of the Fittest in Cities: Urbanisation and Inequality","year":2013,"lang":"en","type":"article","venue":"The Economic Journal","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":139,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Economic and Social Research Council","keywords":"Urbanization; Inequality; Economies of agglomeration; Productivity; Survival of the fittest; Economics; Earnings; Incentive; Economic geography; Selection (genetic algorithm); Labour economics; Economic growth; Microeconomics; Finance","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":[0.00119974,0.0002730799,0.0006837311,0.001040953,0.001183197,0.002995298,0.001099963,0.001299572,0.009069506],"category_scores_gemma":[0.005999804,0.0002648715,0.0005155224,0.001610404,0.002965506,0.002439055,0.00333905,0.001094523,0.0004894298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001971557,"about_ca_system_score_gemma":0.0006811127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01690492,"about_ca_topic_score_gemma":0.01962971,"domain_scores_codex":[0.9993871,0.0002397402,0.0000168538,0.0001016774,0.0000344753,0.000220235],"domain_scores_gemma":[0.9962882,0.001623601,0.0009945751,0.0002186632,0.0001794186,0.0006955188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0003439848,0.0002552815,0.3184584,0.0001348039,0.0001962158,0.001366016,0.003840652,0.1742665,0.001060201,0.4582914,0.007322655,0.03446373],"study_design_scores_gemma":[0.0001273989,0.0002437152,0.1515836,0.0001616756,0.0001747504,0.000987371,0.006133339,0.3141209,0.0004782667,0.5168276,0.009041536,0.000119737],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9552754,0.000831678,0.02292775,0.004340543,0.00003326677,0.00003984261,0.0004310857,0.00005319899,0.01606735],"genre_scores_gemma":[0.9966025,0.0002369981,0.0007517245,0.0001070048,0.00001690169,0.00001802535,0.00007317093,0.000009446981,0.002184197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01690492,"threshold_uncertainty_score":0.03361303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03471202869649258,"score_gpt":0.2063724972774778,"score_spread":0.1716604685809852,"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."}}