Determinants of youth not in education, employment or training: Evidence from Sri Lanka
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The presence of a large proportion of youth neither in education, employment, or training (NEET) signals problems in a country’s education and labor market systems, and has wide‐ranging negative consequences, extending beyond the individual to the economy and society. Using Sri Lankan Labour Force Survey data for the year 2016 and binomial and multinomial logistic regression models, in this paper we provide the first estimates of NEET‐related risk factors in Sri Lanka. Key risk factors of becoming NEET include being female, being of ethnic and religious minorities, belonging to the older 20 to 24 age group, having very low or very high levels of education, being illiterate in English, belonging to a low‐income household or one headed by a male, having young children, and living in more remote areas. Our findings hold several important policy implications for reducing the NEET rate in Sri Lanka and engaging more youth in education and in the labor force.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it