{"id":"W2807256018","doi":"","title":"FUSÕES E AQUISIÇÕES SOB A LENTE DA GOVERNANÇA CORPORATIVA: ANÁLISE SOCIOMÉTRICA E BIBLIOMÉTRICA DOS AUTORES DE REFERÊNCIA INTERNACIONAL","year":2017,"lang":"pt","type":"article","venue":"LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)","topic":"Business and Management Studies","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Subject (documents); Scopus; Bibliometrics; Library science; Population; Corporate governance; Web of science; Sociology; Political science; Management; Computer science; MEDLINE; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.003899406,0.001645424,0.001981121,0.003545979,0.006761513,0.005806727,0.006002815,0.001250323,0.0005149388],"category_scores_gemma":[0.006941223,0.001438652,0.001129797,0.00368645,0.0003973445,0.003403739,0.004333783,0.001738883,0.0003258755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001781436,"about_ca_system_score_gemma":0.001981014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005659095,"about_ca_topic_score_gemma":0.0004567457,"domain_scores_codex":[0.9862529,0.001171384,0.002623396,0.002819773,0.004741486,0.002391061],"domain_scores_gemma":[0.9875862,0.001324556,0.004100437,0.003332601,0.002332578,0.001323637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001572209,0.002624494,0.0217987,0.00037192,0.001602024,0.001663113,0.00582711,0.0001206091,0.001407876,0.8298687,0.1055515,0.02759181],"study_design_scores_gemma":[0.003169514,0.000229969,0.0911701,0.0006170777,0.0004153593,0.0005247042,0.002046199,0.003428547,0.0002682235,0.002861436,0.8936952,0.001573609],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1879331,0.01533315,0.07682557,0.3580135,0.02453493,0.006173071,0.00328344,0.002447393,0.3254558],"genre_scores_gemma":[0.9848399,0.002744439,0.002045771,0.0007293772,0.001559598,0.000404749,0.0001442439,0.0001590911,0.007372828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8270072,"threshold_uncertainty_score":0.9996293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07447990752880776,"score_gpt":0.321786251249057,"score_spread":0.2473063437202493,"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."}}