Unemployment as a chronic problem facing young people in Turkey
Bibliographic record
Abstract
The International Labour Organization (ILO) has warned of young workers facing a dangerous mix of high unemployment, increased inactivity and precarious work in developed countries, as well as persistently high working poverty in the developing world. Living in a developing country, Turkey’s young population experiences similar problems with unemployment and the transition to the labour market. Officially unemployment stood at nearly 11% in the first quarter of 2018 and the rate of youth unemployment was higher than twice the average of OECD countries – although the number of young people in higher education skyrocketed. However, the present troubles of Turkey’s youth in the labour market leave many of them questioning the meaning of acquiring an education, as well as the efficiency of the education system itself. It is common knowledge that higher education graduates in the 20–24 age group are more likely to be unemployed than adults who have lesser qualifications and young women are affected worst. Any effort to address the issue will need to draw on our understanding of various factors like labour market structure, demography, gender, and migration dynamics underlying unemployment and labour relations in Turkey today. This article evaluates these factors with a view to assessing their impact on the young generation of Turkey and shed light on youth unemployment and labour issues in the country.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".