{"id":"W4285586408","doi":"10.1016/j.jwb.2022.101371","title":"The effect of geographic scope on growth and growth variability of SMEs","year":2022,"lang":"en","type":"article","venue":"Journal of World Business","topic":"International Business and FDI","field":"Business, Management and Accounting","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Mount Royal University; University of Calgary","funders":"","keywords":"Scope (computer science); Context (archaeology); Heteroscedasticity; Business; Consumption (sociology); Industrial organization; Corporate governance; Economic geography; Economics; Econometrics; Computer science; Geography; Finance; Sociology","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":[0.001327966,0.000129053,0.0002387137,0.001066308,0.0002451244,0.001392763,0.0003801899,0.0005821657,0.003623114],"category_scores_gemma":[0.01140179,0.0001079017,0.0005068492,0.001571506,0.0006331538,0.0008893709,0.001047525,0.0005079799,0.0003997106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003377187,"about_ca_system_score_gemma":0.0003649623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00900831,"about_ca_topic_score_gemma":0.009720995,"domain_scores_codex":[0.9991584,0.0003074506,0.00008201203,0.0001602459,0.0001159343,0.0001759482],"domain_scores_gemma":[0.9670449,0.02151608,0.006614191,0.00125797,0.001526879,0.002039945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002220327,0.00002476483,0.9946998,0.000009537271,0.00009284671,0.0002017829,0.0002769117,0.001348041,0.0006201459,0.0002952489,0.00009350676,0.002115402],"study_design_scores_gemma":[0.000003070978,0.0000470538,0.9977598,0.000005746087,0.00003279526,0.00008124681,0.000523588,0.001085308,0.0001415081,0.0001711423,0.0001439218,0.000004863923],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988985,0.0001252724,0.0000495411,0.00007079414,0.00000201171,7.956631e-7,0.0001281229,0.00000366292,0.0007212839],"genre_scores_gemma":[0.9998096,0.0000223753,0.00000673079,0.000003120877,0.000003615526,3.581621e-7,0.00006101462,0.000001604407,0.00009179099],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00900831,"threshold_uncertainty_score":0.01791179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005412475437679975,"score_gpt":0.2053563466932005,"score_spread":0.1999438712555206,"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."}}