{"id":"W4384559448","doi":"10.1007/s11156-023-01163-2","title":"Industry co-agglomeration, executive mobility and compensation","year":2023,"lang":"en","type":"article","venue":"Review of Quantitative Finance and Accounting","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Economies of agglomeration; Executive compensation; Economic geography; Industrial organization; Business; Compensation (psychology); Corporate finance; Economics; Microeconomics; Corporate governance; Finance","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0009903624,0.0001051722,0.0005062409,0.0001300918,0.0001167515,0.00003253289,0.00006449311,0.00008912549,0.00003187509],"category_scores_gemma":[0.0002209566,0.0001108763,0.00006791614,0.0003728904,0.000111732,0.0003173292,0.00003743135,0.0001551179,0.00005049461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001644468,"about_ca_system_score_gemma":0.00001410053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001947758,"about_ca_topic_score_gemma":0.00001444188,"domain_scores_codex":[0.9989212,0.0000171553,0.0006125184,0.0002905781,0.00003166185,0.0001269495],"domain_scores_gemma":[0.9990708,0.0001287343,0.0005786826,0.000116921,0.00008375393,0.0000210676],"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":[0.000007165529,0.00003303408,0.1337349,0.002299079,0.00006777509,0.000001231111,0.0003356437,0.00004933775,0.00003934328,0.8574644,0.001936781,0.004031302],"study_design_scores_gemma":[0.0006285923,0.0001584599,0.7782004,0.004323241,0.00004942513,0.000004923472,0.0006419439,0.03226417,0.0001260764,0.05770032,0.1252144,0.0006880079],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9419652,0.05102508,0.0004219388,0.002732206,0.00004957072,0.0002449548,0.0001209284,0.00001495964,0.003425113],"genre_scores_gemma":[0.8464671,0.1523282,0.0004681674,0.0005195424,0.0000241733,0.00002111969,0.00004812507,0.000008351251,0.0001151982],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7997641,"threshold_uncertainty_score":0.4521405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06687864139468455,"score_gpt":0.3090341712481813,"score_spread":0.2421555298534967,"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."}}