University-Educated Specialists, the Demand for Them and Their Standing on the Czech Labour Market [Terciárně vzdělaní odborníci a jejich potřeba na českém trhu práce]
Bibliographic record
Abstract
The paper deals with one of the biggest problems currently faced by the Czech labour market: namely, a shortage of labour with regard to specialists who have completed tertiary education. The introduction stresses the importance of knowledge capital both in contemporary society and in a knowledge-based economy. The need for university-educated labour on the Czech labour market was ascertained from the results of a Ministry of Labour and Social Affairs grant project entitled Risk of a Brain Drain in the Czech Republic. The monitoring took place from the 2nd quarter of 2004 to the 2nd quarter of 2008. The results are based on an analysis of quantitative data (classified advertisements for job vacancies on web portals and in the media and statistics on vacancies provided by labour offices) and repeated qualitative surveys (standardised interviews with recruitment agency personnel). Based on these sources, a shortage of specialists with tertiary education in the Czech Republic was identified in terms of sectoral and professional structure and specialisation. The paper goes on to present university-educated workers as subjects of international competition and the reader is briefly introduced to the approaches of different countries to attracting specialists from abroad.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".