Role of Sustainable Development Goals in Combating Youth Unemployment: A Case Study of the Federal Capital Territory (FCT) Abuja, Nigeria
Bibliographic record
Abstract
Globally, inequality has persisted with especially the youths excluded from full participation in economic, political and social activities. Relatedly, youth unemployment has been known to undermine economies, threaten the peace and destabilize communities, if unchecked. This study investigates youth unemployment, using the Federal Capital Territory (FCT), Abuja, Nigeria, as a case study; with a randomly selected sample size of 1,000 unemployed persons, in the 18–49-year-old age group. It examines the causes of youth unemployment as well as levels of awareness of the UN’s SDG-4 (Quality Education) and SDG-8 (Decent Work) in the working-age population, and the roles of these SDGs and government in combatting unemployment. Frequency and average-mean descriptive statistics of the factors causing youth unemployment indicated low levels of education, lack of employable skills and experience, and poor policies, etc., as predominant causative factors. Regarding the SDGs, the results revealed a low level of awareness and attainment in the population sampled. Education is central to achieving the SDGs; which can, in turn, mitigate unemployment and impel decent work. The introduction of private sector-driven, government-initiated mandatory one-year skills acquisition and developmental schemes for the youths as well as the provision of soft loans for participants to facilitate entrepreneurial ventures are recommended to reduce youth unemployment and promote economic development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".