OHS Standard Evaluation in the Subway Construction in Iran
Bibliographic record
Abstract
One of the most important aspects of health and safety approach is called an occupational, health, and safety management system (OHSM), where deficiencies existing in its procedures and policies, especially in the construction industry, may cause negative consequences such as heavy life and investment losses. In this paper, comparison between Alberta's occupational, health and safety system as an acceptable standard and that of tehran urban and suburban railway company (TUSRC), as a selected Iranian subway construction company, is carried out to identify deficiencies existing in TUSRC OHS policies. For this purpose, 68 basic criteria of successful OHS systems comprising eight categories are identified. This criterion through literature review and interviews with OHS professionals and an auditing tool was established. A questionnaire survey was conducted in selective case studies to find weak points in the current OHS system. Based on these findings, Hazard identification and assessment and Hazard control were identified as the most important categories. From statistical analysis the two categories which could not get acceptable values in TUSRC are Hazard control and Program administration. At the end, a list of remedial action plans is recommended in order to enhance the examined OHSM which could be helpful in similar cases.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".