Safety and Multi-employer Worksites in High-risk Industries: An Overview
Bibliographic record
Abstract
This paper focuses on safety on multi-employer worksites in high-risk industries. Relevant industries are those that utilize flexible labour arrangements and specialization, such as construction, mining and petroleum production, and that traditionally have been high-risk due to hazards in the physical work environment and the occurrence of unsafe work processes and practices. These industries also share common characteristics in matters of overall work environments, multi-employer worksites (including subcontracting chains), as well as tasks performed by contractors, making it relevant to explore and clarify the situation regarding the safety of the affected groups. A comprehensive review is performed of 43 peer-reviewed research articles published up until early 2015, with a main focus on international studies covering safety issues on multi-employer worksites in construction and industrial work settings such as mining, petroleum production and manufacturing. The results show that previous research has focused on a number of key issues that may be divided into three broad categories: 1- contract work characteristics; 2- structural/organizational factors and conditions; 3- cultural conditions. Much of the focus is on structure and organization, for example, how multi-employer arrangements can lead to breakdowns in communication and overall disorganization effects in relation to safety. There is, however, a need for further studies on the nature of these structural and organizational factors and conditions, such as focused studies on the consequences of power asymmetry for the ability of contractors to adhere to safety laws and regulations. Furthermore, we argue that the development towards blurred organizational boundaries in these networks due to extensive outsourcing and long-term contracts may be a worthwhile avenue for future research into safety on multi-employer worksites.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".