Global Research Patterns on Geomechanical Strength Parameters and Slope Stability: A Bibliometric Assessment
Bibliographic record
Abstract
The objective of this paper is to undertake a bibliometric assessment to identify and interpret global patterns within the scientific literature related to geomechanical strength parameters and slope stability using the Dimensions database from 2016 to June 2025. VOSviewer 1.6.20 tool was employed to generate visualization networks for the most prolific authors, documents, and journals on the topic. Further analysis was done to examine both the institutional and country co-authorship patterns as well as keyword co-occurrence and thematic clusters. Results indicated a steady growth in scholarly output especially between 2023 and 2024, with increased participation from newer researchers. Citation metrics results further showed that the International Journal of Rock Mechanics and Mining Sciences was the most prolific journal, while the State Key Laboratory of Geomechanics and Geotechnical Engineering emerged the most prominent institution on the topic. Country co-authorship collaborations revealed China as the leading country in both global collaborations and research productivity, supported by strong bonds with the United States, Australia, and Canada. In contrast, the absence of African countries and institutions in the collaboration networks was striking, highlighting geographic and structural disparities in the global research sphere. The study recommends stronger global research integration, particularly inclusion of underrepresented regions, and strengthening links between emerging and pioneering researchers.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.131 | 0.148 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".