MERGERS AND ACQUISITIONS IN THE LENS OF CORPORATE GOVERNANCE: SOCIOMETRIC AND BIBLIOMETRIC ANALYSIS OF INTERNATIONAL REFERENCE AUTHORS
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.074 | 0.085 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".