Safety Perception and its Effects on Safety Climate in Industrial Construction
Bibliographic record
Abstract
Safety Perception and its Effects on Safety Climate in Industrial Construction G. Eaton, L. Song, N. Eldin Pages 812-820 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: Safety management is an important mortal and business function in construction. Contractors have traditionally tracked and reported lagging indicators, e.g. fatalities and lost-time accident rates, to measure their safety performance and stay in compliance with relevant regulations. Over the last several decades, contractors became more proactive in their approach to safety and developed programs that track leading indicators, e.g. safety audits and safety climate. In particular, safety climate measures workers' perception of safety management and its effectiveness in the workplace. Past literatures indicated that, while some industrial construction contractors ignore safety climate measures, others are limited by a lack of a formal means to measure safety climate. In this study, a survey approach is used to measure an industrial construction contractor's safety climate through three key areas: management commitment, job control, and general safety climate. As a pilot study, a total of 214 individuals at a fabrication facility participated in the survey to verify the validity and effectiveness of the proposed survey approach. This survey study also confirms that job control and management commitment have a positive correlation and that worker demographics have an effect on respondents' perceptions of management commitment. Keywords: Safety climate, job control, management commitment, survey, industrial construction DOI: https://doi.org/10.22260/ISARC2013/0088 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".