Measuring employment precariousness in the European Working Conditions Survey: The social distribution in Europe
Bibliographic record
Abstract
BACKGROUND: Precarious employment is becoming an increasingly important social determinant of health inequalities among workers. The way in which contemporary employment arrangements and their health consequences are addressed in empirical research is mostly based on the contract-related or employment instability dimension. A broader conceptual approach including various important characteristics of the degrading of employment conditions and relations is needed. OBJECTIVE: The general objective of this paper is to empirically test a new multidimensional construct for measuring precarious employment in an existing database. Special focus is on the social distribution of precarious employment. METHODS: A subsample of 21,415 participants in the EU-27 from the Fourth European Working Conditions Survey-2005 was analysed. A cross-sectional study of the social distribution of precarious employment was conducted through the analysis of proportional differences according to gender, social class and credentials for the European Union as a whole and within each country. The 8 dimensions of the employment precariousness construct were represented by 11 indicators. RESULTS: In general, women, workers without supervisory authority, those with fewer credentials, and those living in Eastern and Southern European countries suffer the highest levels of precarious employment. Exceptionally, men, workers with supervisory authority and those with the highest credentials suffer the highest levels of long working hours, schedule unpredictability and uncompensated flexible working times. CONCLUSIONS: This article offers the first validation for an innovative multidimensional conceptualisation of employment precariousness applied to the analysis of existing survey data, showing the unequal distribution of precarious employment across the European labour force. This set of indicators can be useful for monitoring precarious employment.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".