Scientometric Study of Corporate Communication Research in G20 Countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it