Identifying Key References and International Trends in Transparency: A Scientometric Analysis of Scientific Productions over the Last Decade
Bibliographic record
Abstract
Objective Transparency is an interdisciplinary concept that has emerged in multiple management fields such as governance, economy, health, law, media and even religious institutions. Examining the structure of knowledge, developments and current trends in the field of transparency indicates the change of informational approaches (focused on accessibility and disclosure of information) to emerging social approaches. The aim of this study is to identify key references and international trends of the transparency phenomenon at the international level. Accordingly, trends in publication and citation, main actors at the country and organizational levels, leading media outlets including publishers and journals, and established and emerging research fronts based on topics and key terms in the field of transparency have been identified. Methods To extract related scientific productions, the Web of Science Core Collection was utilized, encompassing databases and citation indexes of social and human sciences in journals, conferences, and books. Queries were formulated based on a combination of Title, Author Keywords, and Keyword Plus, refined by the research area of economics, spanning the years 2013 to 2022. Data processing, analysis, and visualization followed scientometric principles, techniques, and concepts. The Clarivate database was consulted for bibliographic data, content, and citation data of scientific productions, while the JCR database was used for data related to scientific journals. Network and overlay maps for citations, co-occurrence of keywords, and their clustering were conducted using the VOS Viewer software. For co-occurrence calculations, a full counting method was applied with threshold. Results Analyzing 1,163 scientific outputs indexed in the field of transparency revealed a dynamic trend with an average annual growth rate of 9.38% in publications and 40.27% in citations. In the last year, the rate of publications in this field surpassed double the average. The standings of participating countries, based on their production share, the volume, and age of received citations, were evaluated to monitor their competitive behavior. Institutions such as the University of London, National Bureau of Economic Research, University of California, European Central Bank, and Udice French Research Universities emerged as the most active in the transparency domain. In this collection, 30% of the works were published in 5% of the journals, as well as top publishers being introduced. The analysis identified key thematic axes at three levels: Research Areas, Citation Topics Meso, and Citation Topics Micro. The top ten Citation Topics Micro, in order, are: Monetary Policy, Corporate Governance, Economic Growth, Option Pricing, Cooperation, Elections, Public Administration, Health Technology Assessment, Antitrust, and Corporate Social Responsibility. Finally, a total of 145 co-occurring keywords were categorized into five clusters, and the trends of leading countries (USA, UK, Germany, China, France, Netherlands, and Canada) in these concepts were compared. Conclusion Examining the production status of countries, the dynamic rankings of participation in outputs, the volume and age of received citations, and the unique thematic trends of each country, underscores the importance of national planning for success in transparency management. On the scientific development path, identifying key references in transparency paves the way for productive research advancements and interaction with transparency leaders, and familiarity with current trends in this domain facilitates efforts towards promising directions. The composition of the top identified organizations, encompassing academic, non-profit, and supranational entities, suggests that success in the transparency realm necessitates regulatory measures for a balanced growth of academia, market, and industry. The knowledge structure of the transparency field highlights the necessity to consider a broad spectrum of stakeholders, both legal and natural individuals, in roles such as policymakers, business managers, and researchers within the transparency ecosystem. Universal thematic axes and country-specific indigenous axes provide insights for devising research agendas, enabling us to align with global understanding in addressing our top-priority issues.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.028 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.079 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".