Economic Performance and Unemployment in the Czech Republic
Bibliographic record
Abstract
The Labor market is very important for the overall functioning of the economy. Unemployment rate belongs to the four most important characteristics of the economic efficiency along with gross domestic product, balance of payments and inflation rate. It is incorrect to think that unemployment is a problem only of the unemployed people and their family. High unemployment has many negative consequences - from economic to business, governmental and private. The economic consequences include a decline of the GDP, an increase in the relative poverty and a fall of the potential GDP. Considering businesses, there is a negative impact as well as a positive one. The negative impact is represented by a decline in demand for goods and services. On the other hand, there are a higher number of job seekers from which the firm can select the most suitable ones. Government deals mainly with a fall of revenues and a raise of expenditures. Job is deeply embedded in our culture as well as in our psyche. Through employment, we define who we are. From the individual perspective, losing job has many unpleasant effects such as a drop in living standards due to the loss of income, social exclusion, loss of a social status, deterioration of family relationships and much more. The current economic situation in the European Union changes the type of unemployment in recent years. Text of the article addresses the recessional economic developments in the Czech Republic and unemployment rate of the economically active population associated therewith. The objective is to chart the development of the basic indicator of the functioning of the economy, identify the different types of unemployment in the Czech Republic by the Beveridge curve and through correlation analysis statistically evaluate the relationship between the unemployment rate and the growth rate of real gross domestic product.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".