Behavior based safety in projects: A bibliometric analysis of global literature
Bibliographic record
Abstract
Behavior-Based Safety (BBS) is the art of communicating and correcting unsafe behaviors of project and factory staff. It was developed from behavioral sciences through direct observations of behavior changes. Scholars across the globe have examined the efficacy of BBS in ensuring safe behaviors in projects. However, the available literature is fragmented, and there is a need to have a panoramic, systematic, and comprehensive understanding of BBS. This study synthesizes, analyzes, and categorizes the BBS literature to provide a conceptual framework and inputs for further research. The study was conducted using bibliometric analysis of 56 research articles from the Scopus database. The results present a bird's-eye view of published materials on BBS. The bibliographic coupling helped categorize the outcomes of BBS into various clusters. The study also found that most highly cited articles were from the construction industry. Future researchers may examine the findings of seminal studies, identified as the most cited documents, to inform design choices and trade-offs that address the major hindrances to implementing BBS in its true spirit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.263 | 0.266 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".