How to Reduce the Unemployment and the Poverty in Djibouti: Another Alternative
Bibliographic record
Abstract
The Unemployment and the poverty are problems which exist throughout in the world. Many methodologies and policies are proposed in the literature for defeating them. The Republic of Djibouti is among these countries which has the problems of the unemployment and the poverty. For fighting the unemployment and the poverty, the government of Djibouti takes many initiatives such as the Strategic Document for Reducing the Poverty (SDRP) in 2003 and National Initiative of Social Development (NISD) in 2007 and created agencies for promoting the entrepreneurships. Despite of all these measures, the unemployment and the poverty rate still remain high critical level. In this paper, we propose another alternative: the classification of unemployed persons, the creation of service industries by structuring the informal jobs and a manner to create manufacturing industries.Our proposed methodology allows the reducing of the unemployment rate of order 6% (six percent) and if we apply it on all informal jobs in Djibouti, the unemployment rate will decrease of order 20% at 25% (twenty to twenty five percent). We also discuss how to update the used measures till today.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".