{"id":"W4286665675","doi":"10.5465/ambpp.2022.14114abstract","title":"Knowledge Generation and Knowledge-, Individual-, and Interfirm-Level Network Structural Features","year":2022,"lang":"en","type":"article","venue":"Academy of Management Proceedings","topic":"Innovation and Knowledge Management","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Knowledge management; Social network analysis; Network structure; Business; Knowledge creation; Organizational network analysis; Network analysis; Multilevel model; Computer science; Organizational learning; Marketing; Engineering","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":[0.0008883876,0.0001359811,0.0001460818,0.001994678,0.0007159997,0.001981579,0.0003700605,0.0003967953,0.004801748],"category_scores_gemma":[0.006907161,0.00007944715,0.0002038553,0.001863734,0.0009237138,0.002744433,0.001390357,0.0003910165,0.0002784459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001240711,"about_ca_system_score_gemma":0.0006799969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003209333,"about_ca_topic_score_gemma":0.005012452,"domain_scores_codex":[0.9994255,0.0001457851,0.0000347192,0.0001083082,0.0001452885,0.0001404809],"domain_scores_gemma":[0.9941031,0.002723221,0.001756141,0.0003922495,0.0005220585,0.000503292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001183737,0.0001477445,0.8347158,0.0001536273,0.0001362513,0.0003675868,0.005053851,0.005710187,0.001720176,0.07200442,0.001082611,0.07878938],"study_design_scores_gemma":[0.0000177624,0.00009086317,0.9095848,0.000095032,0.00009932549,0.0002891453,0.0069033,0.01731793,0.001051735,0.05639755,0.008121765,0.00003093313],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9613619,0.0003084274,0.006116442,0.0005952083,0.00000989067,0.00003691507,0.0002926899,0.00002609331,0.03125235],"genre_scores_gemma":[0.9989935,0.00006121797,0.0003674006,0.000008937628,0.000004131659,0.000007430202,0.00007356707,0.000001351474,0.0004824437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004801748,"threshold_uncertainty_score":0.01606345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0525070548895907,"score_gpt":0.2725021456007307,"score_spread":0.21999509071114,"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."}}