Makroekonomiczne determinanty bezrobocia na przykladzie Polski i Stanow Zjednoczonych w latach 2000–2016 [Macroeconomic determinants of unemployment on the example of Poland and United States in years 2000–2016]
Bibliographic record
Abstract
Motivation: The motivation for choosing a subject was high importance of long-term unemployment for economic growth and methods of unemployed profiling to prevent that phenomenon. Aim: The aim of the article was the selection of macroeconomic determinants of long-term unemployment in Poland and the United States. The analysis was also focused on discussing the possibility of combating long-term unemployment through the unemployed profiling and the current results of this method in Poland. Results: The following macroeconomic variables had a statistically significant impact on the unemployment rate: the unemployment rate in the previous quarter, real GDP growth rate, inflation rate, growth rate for export share in GDP and investments. In most cases, this influence was in line with economic theory. In Poland, the unemployment rate is most responsive to changes in GDP in the opposite direction. It can be expected that stimulating economic growth or taking actions supporting it in the long run, should translate into a decrease in the unemployment rate in Poland. In the United States, growth rate for export share in GDP and investments had a significant impact on unemployment rate.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".