A bibliometric analysis of safety performance in the government sector
Bibliographic record
Abstract
This study conducts a bibliometric analysis focusing on safety performance within the government sector, drawing on data from Scopus and Web of Science (WoS). Analyzing publication productivity, thematic areas, influential authors, leading research institutions, and prevalent keywords, our findings reveal a substantial increase in safety performance publications, particularly notable trends emerging in recent years. Key themes include "road safety," "risk management," and "safety culture," reflecting evolving priorities within governmental safety performance research. Additionally, "factor analysis" is observed alongside "safety climate" and "construction safety," suggesting a methodological shift in examining safety practices within construction-related governmental activities. Furthermore, "benchmarking" is associated with various safety domains, indicating a holistic approach to safety performance improvement. Mapping research collaboration among authors from different countries unveils distinct clusters, highlighting regional partnerships and global networks. Notably, Canada appears as an isolated cluster, while Europe demonstrates a collaborative network, and Southeast Asia and Oceania exhibit regional cooperation. East Asian countries also showcase collaboration, as do countries from different continents, emphasizing global partnerships in safety performance research. These collaborative efforts are crucial for advancing safety performance, knowledge, sharing best practices, and addressing common challenges within governmental contexts. This analysis offers valuable insights for policymakers, practitioners, and researchers interested in enhancing safety performance and fostering a culture of resilience within government entities.
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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 | 0.014 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.197 | 0.286 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".