{"id":"W2765886626","doi":"10.1080/08276331.2017.1391368","title":"Antecedents of SME embeddedness in inter-organizational networks: Evidence from China's aerospace industry","year":2017,"lang":"en","type":"article","venue":"Journal of Small Business & Entrepreneurship","topic":"Entrepreneurship Studies and Influences","field":"Business, Management and Accounting","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Embeddedness; Centrality; Business; Position (finance); Diversity (politics); Aerospace; China; Industrial organization; Empirical evidence; Empirical research; Knowledge management; Contrast (vision); Marketing; Computer science; Political science; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006935241,0.0003543652,0.0007132956,0.0003505499,0.0002574582,0.0006189231,0.001626159,0.0002520239,0.0003015613],"category_scores_gemma":[0.002834482,0.0003061263,0.0001639643,0.0006141325,0.0002535194,0.002677773,0.0007082163,0.0006474403,0.00001436574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000588587,"about_ca_system_score_gemma":0.00008512937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002789901,"about_ca_topic_score_gemma":0.001198184,"domain_scores_codex":[0.9974778,0.00004728926,0.001078045,0.0004156786,0.0005561822,0.0004249645],"domain_scores_gemma":[0.9951096,0.0001958558,0.002869764,0.0006707955,0.001108578,0.0000453846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002332035,0.000205146,0.9910983,0.0001080657,0.00006863413,0.00009847099,0.0001794043,0.005131379,0.0006125738,0.0001585117,0.0003521711,0.001754129],"study_design_scores_gemma":[0.0009764816,0.00002066172,0.9936664,0.0026414,0.0001150398,0.000012761,0.0002777383,0.0005494723,0.0003467872,0.0009627796,0.0001464005,0.0002840662],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924886,0.0009020692,0.0004328982,0.003663881,0.001971529,0.0001536448,0.000004302859,0.00002146468,0.0003616509],"genre_scores_gemma":[0.9973686,0.0004502009,0.0001476764,0.0002558391,0.001662485,0.000003756051,0.000006349216,0.00003813332,0.00006689846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004880102,"threshold_uncertainty_score":0.9999391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03610454455565752,"score_gpt":0.2606421950758017,"score_spread":0.2245376505201441,"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."}}