What should we know about precarious employment and health in 2025? framing the agenda for the next decade of research
Bibliographic record
Abstract
The generalization of flexible labour markets, the declining influence of unions and the degradation of social protection has led to the emergence of new forms of employment at the expense of the Standard Employment Relationship, as well as a considerable amount of research across social and scientific disciplines. Years ago we suggested the urgent need to disentangle the consequences of new types of employment for the health and well-being of workers, contending that the study of precarious employment and health is in its infancy. Today, research challenges include clearer, more precise definitions of the original concepts, a more detailed understanding of the pathways and mechanisms through which precarious employment harms worker health, stronger information systems for monitoring the problem and a complex systems approach to employment conditions and health research. All of these must be guided by the theoretical and policy debates linking precarious employment and health, and be geared towards developing better tools for the design, implementation and evaluation of policies intended to minimize precariousness in the labour market and its effects on public health and health inequalities. Our aim in this paper is to outline an agenda for the next decade of research on precarious employment and health, establishing a compelling programme that expands our understanding of complex causes and links.
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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 | 0.038 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.012 | 0.029 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.026 | 0.024 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".