Cykliczne i strukturalne determinanty wzrostu bezrobocia w Unii Europejskiej w okresie niskiej koniunktury gospodarczej
Bibliographic record
Abstract
The objective of the paper was to evaluate the contribution of structural changes in labor demand to an increase of unemployment in the European Union as a whole and in its individual member states. Analysis of changes in sectoral employment dynamics using a Lilien Index and Beveridge curve shifts shows that in the years 2008–2013 the rise in unemployment was driven by an aggregate shock, with a mostly cyclical joblessness pattern. Among the six largest labor markets within the EU, the most significant increases in structural unemployment occurred in Spain, followed by France, while no such phenomenon was identified in Germany, Italy, Poland, and Great Britain. At the same time, it should be noted that the observed labor market trends do not preclude the intensification of corporate restructuring during periods of economic slowdown, which may contribute to structural unemployment.
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How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".