{"id":"W2103405033","doi":"10.1504/ijkbd.2012.045558","title":"An aggregating approach to ranking cities for knowledge-based development","year":2012,"lang":"en","type":"article","venue":"International Journal of Knowledge-Based Development","topic":"Global Urban Networks and Dynamics","field":"Social Sciences","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Ranking (information retrieval); Index (typography); Regional science; Urbanization; Geography; Variety (cybernetics); Rank (graph theory); Nice; Business; Economic growth; Computer science; Economics; Information retrieval; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003499966,0.0002919269,0.0003677854,0.0005305728,0.000622561,0.0002531681,0.001057634,0.0001508603,0.00004674955],"category_scores_gemma":[0.0004982465,0.0002769342,0.000184316,0.0003581362,0.0001122025,0.0004505773,0.00007435188,0.0002230728,0.00003380396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001770803,"about_ca_system_score_gemma":0.003789475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001958793,"about_ca_topic_score_gemma":0.000278968,"domain_scores_codex":[0.9967933,0.0002150047,0.001019621,0.0002617576,0.0009906882,0.0007196707],"domain_scores_gemma":[0.995979,0.0004714124,0.0005601997,0.0001374977,0.002228962,0.0006229573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001239529,0.006434737,0.06296469,0.000220183,0.0009525208,0.00001613595,0.1584436,0.03895914,0.0002180364,0.03300899,0.01450983,0.6830326],"study_design_scores_gemma":[0.003428443,0.0001639229,0.01016789,0.0009637162,0.00005850007,0.00001221128,0.00487058,0.01271658,0.001954709,0.0003156427,0.9643797,0.0009680495],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1953958,0.001866715,0.7650746,0.0006716353,0.007600659,0.0008665866,0.00001374654,0.0001115993,0.02839869],"genre_scores_gemma":[0.7875835,0.000005028079,0.2098446,0.0003592339,0.001661959,0.00006066963,0.00003586938,0.00003069641,0.0004184596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9498699,"threshold_uncertainty_score":0.9999683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04156484286869253,"score_gpt":0.3320540145891498,"score_spread":0.2904891717204572,"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."}}