{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006061248,0.0002722101,0.0004243962,0.002621907,0.0006029228,0.002182969,0.0004573275,0.0006141168,0.004875631],"category_scores_gemma":[0.002593046,0.0001324695,0.0003101424,0.007414548,0.001876645,0.002056284,0.0009401754,0.0005815648,0.0005659843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001618731,"about_ca_system_score_gemma":0.001005495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02141672,"about_ca_topic_score_gemma":0.03803566,"domain_scores_codex":[0.9995627,0.0000983092,0.00002404525,0.0000913742,0.000105803,0.0001176944],"domain_scores_gemma":[0.998287,0.0005175803,0.0006651752,0.00009120703,0.0002962287,0.0001428828],"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":[0.0001523093,0.0001427746,0.2407349,0.0009596823,0.0005898115,0.0005894019,0.00274694,0.01337781,0.000722972,0.4412493,0.03107606,0.267658],"study_design_scores_gemma":[0.00002105421,0.000064313,0.5670886,0.000531164,0.0002288206,0.0007383532,0.006893294,0.005147082,0.0004327066,0.268075,0.1507307,0.00004893702],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5515923,0.3186792,0.009659423,0.02119645,0.0003478573,0.00004191756,0.00112852,0.0000868818,0.09726757],"genre_scores_gemma":[0.9358415,0.05244714,0.001060028,0.0002666269,0.0004927797,0.000009224231,0.0003730749,0.00001276377,0.009496848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02141672,"threshold_uncertainty_score":0.04258412,"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."}}