{"id":"W2133577845","doi":"10.5539/ibr.v5n10p107","title":"The Life Cycle of Growth Path among Micro Firms: Swedish Data","year":2012,"lang":"en","type":"article","venue":"International Business Research","topic":"Firm Innovation and Growth","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sample (material); Growth rate; Econometrics; Demographic economics; Business; Economics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009090364,0.0004146496,0.0006094815,0.005781905,0.0004169111,0.001641483,0.0005434435,0.0006889963,0.002569148],"category_scores_gemma":[0.005072786,0.0003148957,0.0005491158,0.007127098,0.0003816256,0.0005063257,0.0009369249,0.0004961575,0.001651146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008355547,"about_ca_system_score_gemma":0.001139445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02912232,"about_ca_topic_score_gemma":0.02208561,"domain_scores_codex":[0.9989108,0.000172051,0.000186409,0.0002371724,0.0003145834,0.0001790092],"domain_scores_gemma":[0.9932202,0.002928692,0.002190718,0.0005822519,0.0007489932,0.0003290851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004055806,0.0002293546,0.9524558,0.0003415334,0.0001648769,0.0009447255,0.001169563,0.008553548,0.0005060681,0.001337924,0.007118517,0.02677258],"study_design_scores_gemma":[0.00004765738,0.0001801162,0.9571617,0.0001685365,0.0001021169,0.0007550121,0.003177368,0.00564861,0.001242486,0.001450483,0.03000841,0.00005741794],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9574932,0.000761795,0.00080628,0.0001032906,0.00001562792,0.00005499039,0.03845073,0.00006298189,0.002250957],"genre_scores_gemma":[0.8853076,0.0009870846,0.001904157,0.00003884166,0.00002650742,0.0001830892,0.109099,0.00003311163,0.002420689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02912232,"threshold_uncertainty_score":0.05790561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.135390960326041,"score_gpt":0.3412378098640036,"score_spread":0.2058468495379626,"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."}}