Evolution of Financial Development Research: A Bibliometric Analysis
Bibliographic record
Abstract
This study aims to analyze publications on financial development between 1986 and 2023 using bibliometric analysis methods. The analysis, based on data obtained from the Web of Science database, utilizes bibliometric tools such as keyword analysis, author collaboration networks, citation analysis, and bibliographic coupling to identify trends, key authors, influential journals, and emerging research topics in the field. The results indicate that financial development research is predominantly concentrated in the fields of economics, environmental sciences, and business finance, with economics having the highest number of publications. A significant increase in publications is observed after 2014, particularly after the COVID-19 pandemic. VOSviewer and R Studio programs were chosen in the study due to their strengths in terms of functionality. According to the results, the countries with the most citations were China, the USA, and Pakistan. The most cited authors are Shahbaz M. with 3926 citations, Zingales I. with 3252 citations, and Oztürk I. with 2710 citations. The authors in the top two are also in the top two in terms of total link strength. The analysis shows that key themes such as economic growth, energy consumption, CO2 emissions, and renewable energy have increasingly intersected with financial development, highlighting the growing focus on sustainability. China, Pakistan, and the USA are the most active countries in financial development research, with China leading both in terms of publication count and citations.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.109 | 0.169 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| 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".