Examining the Research Taxonomy of Credit Default Swaps Literature Through Bibliographic Network Mapping
Bibliographic record
Abstract
This study presents a bibliometric analysis, using spatial approach, of 943 articles from 2003 to March 2025 showing the growing importance of CDSs in the literature and their role in credit risk management. The Web of Science’s Core Collection database was used for bibliometric mapping. The bibliographic data were grouped and analyzed using VOSviewer to create network visualization maps that included country-wise, document-wise, and source-wise citations analysis, bibliographic coupling, and the co-occurrence of keywords. Subsequently, significant terms were identified through the analyses where risk assessment, risk management, and credit derivatives were found to be the most used keywords. Further, USA turns out to be the country where the most research was published on CDSs with maximum citations, highlighting the growing popularity of this research topic in this region. In addition, bibliographic coupling appears to capture information from 13 clusters formed during the analysis on bibliographically linked documents with their link strength. The bibliometric analysis of the CDS literature illustrates the intellectual framework of research on this topic, traces the progression of the research topic over time, and identifies the areas where this research field might develop in the future.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.173 | 0.180 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".