Employment and Labor Market Results of the SOPHIE Project
Bibliographic record
Abstract
This article reports evidence gained by the SOPHIE Project regarding employment and labor market-related policies. In the first step, quality of employment and of precarious and informal employment in Europe were conceptualized and defined. Based on these definitions, we analyzed changes in the prevalence and population distribution of key health-affecting characteristics of employment and work between times of economic prosperity and economic crisis in Europe and investigated their impact on health outcomes. Additionally, we examined the effects of several employment and labor market-related policies on factors affecting health equity, including a specific analysis concerning work-related gender equity policies and case studies in different European countries. Our findings show that there is a need to standardize definitions and indicators of (the quality of) employment conditions and improve information systems. This is challenging given the important differences between and within European countries. In our results, low quality of employment and precarious employment is associated with poor mental health. In order to protect the well-being of workers and reduce work-related health inequalities, policies leading to precarious working and employment conditions need to be suspended. Instead, efforts should be made to improve the security and quality of employment for all workers.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".