LOW PAY, IN‐WORK POVERTY AND ECONOMIC VULNERABILITY: A COMPARATIVE ANALYSIS USING EU‐SILC*
Why this work is in the frame
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Bibliographic record
Abstract
We explore the potential of data from EU‐SILC (‘Statistics on Income and Living Conditions’) for the enlarged European Union for the study of low pay and its relationship to household poverty and vulnerability. Limitations of the earnings data currently available mean the analysis covers only 14 of these countries. For employees who are not low paid, income poverty is seen to be rare. The low paid face a much higher risk of being in a household below relative income poverty thresholds, ranging from 7 per cent in Belgium and the Netherlands up to 17–18 per cent in Austria, Estonia and Lithuania. The likelihood of their being in a poor household is clearly linked to gender, age and social class. In most of the countries only a minority of low‐paid individuals are in vulnerable households.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it