The Impact Of Education On The Duration Of Unemployment Under Conditions Of Imbalanced Labour Market
Bibliographic record
Abstract
The article presents the results of à study of the relation between education and the duration of unemployment on a labour market functioning in different economic conditions. The aim of the study is to examine the d egree of influence of the level of education on the chance of long-term unemployment under conditions of a balanced and a destabilized in time of economic crisis labour market. The estimation of the effect of the impact of the level of education on the dur ation of unemployment has been accomplished on the basis of individual data 1 on the economically active individuals, gathered by means of the sample "Survey of the Labour Force" conducted by the National Statistical Institute. The data on the unemployed pe rsons and their socio - demographic characteristics are as of the fourth quarter of 2008 and the fourth quarter of 2013 and include 14, 971 and 14, 652 monitored individuals, respectively. Using logistic regression it has been ascertained that the relation bet ween education and the duration of unemployment of the persons who are out of work manifests itself clearly under a stable labour market. In an unstable economic env i- ronment the effect of the impact of the education on the duration of unemployment decrease s to insignificant levels. The existence of a disequilibrium between the demand and supply on the labour market in a period of economic instability obliterates the role of the higher education level for reducing the duration of unemployment of the persons who are out of work.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".