Measuring the Unemployment Risk in Northern Greece from the LFS Micro-Data during the Period 1994-2006
Bibliographic record
Abstract
This paper investigates unemployment risk and job prospects of individuals in the three Northern Greek regions (Central Macedonia, Western Macedonia, and Eastern Macedonia and Thrace), during the implementation of the second (1994-99) and the third (2000-06) Community Support Frameworks. More specifically, the research focuses on the social and demographic characteristics that increase the chances of individuals in finding a job, and explores the impact of gender, age, marital status, residence location, level of education, immigrant status, registered in the Manpower Employment Organization (OAED) and participation in training courses. Furthermore, there is an investigation whether University graduates face greater difficulties in finding a job than non-University graduates, as a series of studies or aggregate statistics for Greece conclude. Sampling is based on individual anonymized records (micro-data) of the Labour Force Survey for both employed and unemployed at Nomenclature of Territorial Units for Statistics-2 level. The findings of the logit model are mixed for all the variables used, apart from those of registered in OAED for which the results have no differences among regions and years.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".