Bibliometric analysis of occupational health in civil construction works
Bibliographic record
Abstract
Objective: to perform a bibliometric analysis of occupational health in civil construction works in areas of focus and significant contributions in the last 10 years (2013-2023) worldwide indexed in Scopus. Methodology: A quantitative bibliometric analysis was undertaken. The indicators of scientific waste were generated by means of 100 documents selected in Scopus using the keywords in English ("occupational health" AND "civil construction") from 2013 to 2023.Results: There was a 27.90% growth in publications on the subject by the year 2020, which indicates a strong interest in the subject. Portugal is one of the countries with more scientific production (n=74; 20.67%), and the University of Lisbon with more publications (n=14). The journal Material Science and Engineering: R Reports received 96 citations with the author being Salas, J. Conclusions: The bibliometric analysis of occupational health in civil construction works during the last 10 years (2013-2023) has provided valuable insight into the areas of focus and significant contributions in this field. The data reveal a steady increase in research output, with a notable peak in the period studied. It has been observed that several nations, including Portugal, Canada and Mexico, have contributed significantly to the scientific output in this field.
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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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.155 | 0.193 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".