Safety and health among undeclared workers: A mixed methods study investigating social partner experiences and strategies
Bibliographic record
Abstract
Little is known about the experiences of the social partners in helping undeclared workers resist Occupational Safety and Health (OSH) issues. This study draws upon Walter Korpi’s ‘power resource theory’ to gain a deeper understanding of how power resources within the construction, transport, and cleaning sectors influence the ability of social partners to respond to OSH issues related to undeclared work. This mixed-method study uses survey data from employer representatives in the construction (n = 686) and transport (n = 650) sectors in Sweden in 2019 to estimate the nature and magnitude of undeclared work-related problems. To also study the view of union representatives, a duplicate survey was sent to union representatives in the transport, construction, and cleaning sectors (n = 57) in 2020, followed by 13 semi-structured interviews with Regional Safety Representatives (RSRs) in 2021–2023. Our findings show that employer representatives in construction and transport reported that the violation of OSH regulations was uncommon and remained unchanged, most union representatives said the opposite. We found a gradient of activism among the unions towards OSH issues related to undeclared work dependent on their power resources. Furthermore, structural and organizational factors limited the RSRs’ ability to address undeclared work. The RSRs identified strategies to tackle OSH issues related to undeclared work in their sectors, these included but were not limited to, dismantling the language barrier between unions and undeclared foreign-born workers, for OSH coordinators and main contractors to be held responsible for OSH violations and greater cooperation between the relevant authorities dealing with undeclared work.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".