FUSÕES E AQUISIÇÕES SOB A LENTE DA GOVERNANÇA CORPORATIVA: ANÁLISE SOCIOMÉTRICA E BIBLIOMÉTRICA DOS AUTORES DE REFERÊNCIA INTERNACIONAL
Bibliographic record
Abstract
This study aims to identify existing relational systems between prolific and most referenced authors in research on mergers and acquisitions through the lens of corporate governance at the international level. As for the methodological aspects, there was bibliometric and sociometric analysis of 103 papers in international journals of research ISI Web Knowledge and SCOPUS. The defined population for research consisted of publications that addressed in its title, abstract and keywords the terms "corporative governance" and "mergers and acquisitions", referring to sub-area "business management accounting", the years 1996 to 2013. Obtaining representative figures of the structure of the co-authors network and its indicators, it used the software UCINET® 6. It also formed the word cloud with abstracts of papers downloaded through WORDLE® program available online. As a result, it was observed that publications on the subject began to appear from 2004, and the 14 most prolific authors holds 38.83% of the total findings, it shows concentration of publications in a small group of authors. Under the sociometric analysis, it appears that there was a strengthening of co-authorship networks in the last nine years based on 2013. It is concluded that the discussion on the subject is distributed on small isolated groups of researchers who are affiliated to a number concentrate of US, Canadian and English educational institutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".