Scientometric Study of Corporate Communication Research in G20 Countries
Bibliographic record
Abstract
This study evaluates the research productivity in Corporate Communication among G20 countries. The dataset utilised spans from 1999 to 2022, sourced from the Scopus database. Employing scientometric techniques, the research investigates various aspects of research productivity, including impact, collaboration levels, and keywords, offering a comprehensive overview of publications in this field since the inception of G20 countries’ collaboration. The highest Annual Growth Rate (AGR) was observed in 2004 (130.77), followed by 2000 (84.62) and 2008 (80). Despite a dip in 2020 (-15.38), there was a positive AGR in publications during the pandemic. This study holds particular significance and timeliness as India assumes the presidency for G20, marking a quarter century of G20 collaboration. The study’s findings suggest a positive correlation between authors’ and journals’ h and g indexes, indicating a linear relationship. While the United States boasts the highest number of published documents (n=323), Russia received the most citations (n=2297), highlighting disparities in publication output and impact. The research also outlines future projections, study limitations, and implications. Analysing trends, impact, collaboration, and emerging topics informs strategic decision-making, policy formulation, and resource allocation, pushing the boundaries of current knowledge and revealing potential avenues for exploration.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.063 | 0.119 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".