Experiences of insecurity among non-standard workers across different welfare states: A qualitative cross-country study
Bibliographic record
Abstract
In recent decades, economic crises and political reforms focused on employment flexibilization have increased the use of non-standard employment (NSE). National political and economic contexts determine how employers interact with labour and how the state interacts with labour markets and manages social welfare policies. These factors influence the prevalence of NSE and the level of employment insecurity it creates, but the extent to which a country's policy context mitigates the health influences of NSE is unclear. This study describes how workers experience insecurities created by NSE, and how this influences their health and well-being, in countries with different welfare states: Belgium, Canada, Chile, Spain, Sweden, and the United States. Interviews with 250 workers in NSE were analysed using a multiple-case study approach. Workers in all countries experienced multiple insecurities (e.g., income and employment insecurity) and relational tension with employers/clients, with negative health and well-being influences, in ways that were shaped by social inequalities (e.g., related to family support or immigration status). Welfare state differences were reflected in the level of workers' exclusion from social protections, the time scale of their insecurity (threatening daily survival or longer-term life planning), and their ability to derive a sense of control from NSE. Workers in Belgium, Sweden, and Spain, countries with more generous welfare states, navigated these insecurities with greater success and with less influence on health and well-being. Findings contribute to our understanding of the health and well-being influences of NSE across different welfare regimes and suggest the need in all six countries for stronger state responses to NSE. Increased investment in universal and more equal rights and benefits in NSE could reduce the widening gap between standard and NSE.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".