Mapping the intellectual structure and demystifying the research trend of cross listing: a bibliometric analysis
Why this work is in the frame
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Bibliographic record
Abstract
Purpose This study aims to conduct a comprehensive bibliometric analysis to determine the intellectual structure of cross-listing studies and suggests a road map for future research in this field. Design/methodology/approach A step-by-step procedure was carried out. With the help of a defined search string, 580 articles from reputed journals have been retrieved from the Scopus database. Bibliographic coupling and keyword analysis were executed to understand the current research scenario and future research directions in this research field. In addition, R Studio combined with VOSviewer was employed to analyse and visualise the data. Findings The results provide a deeper insight into publication trends, most prolific countries, institutions and journals in the area of cross-listing. The highest collaboration was observed between the authors in the USA and Canada. Moreover, the results contradict Bradford's and Lotka's laws. A thorough review of the literature identifies five clusters in this domain. Finally, keyword analysis offers a future road map in cross-listing research. Originality/value Researchers have shown greater interest in cross-listing topics over the past decades. Even though the research volume on this subject is increasing, the current retrospective is still insufficient. To the best of the authors' knowledge, this study is the first to provide valuable insights to practitioners, academicians, and prospective researchers about the intellectual structure of cross-listing and also offers future avenues in this research field through bibliometric analysis.
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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 | 0.046 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.458 | 0.933 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it