Conceptualizing the Contextual Dynamics of Safety Climate and Safety Culture Research: A Comparative Scientometric Analysis
Bibliographic record
Abstract
Safety climate and safety culture are important research domains in risk and safety science, and various industry and service sectors show significant interest in, and commitment to, applying its concepts, theories, and methods to enhance organizational safety performance. Despite the large body of literature on these topics, there are disagreements about the scope and focus of these concepts, and there is a lack of systematic understanding of their development patterns and the knowledge domains on which these are built. This article presents a comparative analysis of the literature focusing on safety climate and safety culture, using various scientometric analysis approaches and tools. General development patterns are identified, including the publication trends, in terms of temporal and geographical activity, the science domains in which safety culture and safety climate research occurs, and the scientific domains and articles that have primarily influenced their respective development. It is found that the safety culture and safety climate domains show strong similarities, e.g., in dominant application domains and frequently occurring terms. However, safety culture research attracts comparatively more attention from other scientific domains, and the research domains rely on partially different knowledge bases. In particular, while measurement plays a role in both domains, the results suggest that safety climate research focuses comparatively more on the development and validation of questionnaires and surveys in particular organizational contexts, whereas safety culture research appears to relate these measurements to wider organizational features and management mechanisms. Finally, various directions for future research are identified based on the obtained results.
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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 | 0.043 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.105 | 0.139 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".