{"id":"W2796703512","doi":"10.1111/jors.12436","title":"Manufacturing (co)agglomeration in a transition country: Evidence from Russia","year":2019,"lang":"en","type":"article","venue":"Journal of Regional Science","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Social Sciences and Humanities Research Council of Canada; National Research University Higher School of Economics","keywords":"Economies of agglomeration; Proxy (statistics); Instrumental variable; Estimation; Variable (mathematics); Manufacturing; Industrial organization; Business; Economic geography; Economics; Econometrics; Microeconomics; Statistics; Marketing; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001157883,0.00009242946,0.0003228988,0.0004832783,0.00007161126,0.0001277308,0.0003725955,0.00004793195,0.0002426542],"category_scores_gemma":[0.00004660458,0.00008874435,0.0001217393,0.0003387027,0.0001107525,0.001408092,0.00001649393,0.0001603461,0.0001316575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002938295,"about_ca_system_score_gemma":0.0001186082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001181476,"about_ca_topic_score_gemma":0.0001257889,"domain_scores_codex":[0.9987054,0.0000115684,0.0006737654,0.0002662995,0.0001576915,0.0001852734],"domain_scores_gemma":[0.9989781,0.00008609471,0.0006453251,0.0001443695,0.00005322961,0.00009291418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009377029,0.0005767969,0.5464118,0.00008710528,0.0002640334,0.0001231745,0.006202637,0.1358449,0.02602761,0.2745987,0.001936417,0.006989097],"study_design_scores_gemma":[0.001744115,0.000354451,0.7963218,0.0005276643,0.00001842604,0.0000839045,0.000356522,0.0752811,0.003065086,0.1031226,0.01846888,0.0006554392],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924483,0.0007772711,0.001649566,0.003891288,0.0002349017,0.00006345611,0.00001075088,0.000002530315,0.0009219736],"genre_scores_gemma":[0.9980407,0.0007483216,0.0006364757,0.0003659392,0.0001425941,0.00000115047,0.000002866118,0.000005123599,0.0000568409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.24991,"threshold_uncertainty_score":0.3618889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03466367150858093,"score_gpt":0.2394316385105945,"score_spread":0.2047679670020136,"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."}}