Talent Promotion Programs and Management of Formal Education in Nigeria
Bibliographic record
Abstract
This study examined talent promotion programs and management of formal education among youth in Nigeria. This quantitative study determined the perception of administrators and lecturers on the importance of personality development, sports, music and reality television shows towards effective management of formal education. A purposive sampling technique was used to select 45 participants in three departments at the University of Ilorin, Kwara State, Nigeria. Data was collected using a Talent Promotion Programs and Management of Formal Education Questionnaire (TPPMFEQ), and analyzed using descriptive statistics. The findings revealed that personality development, sports, music and reality television shows are important towards effective management of formal education. The findings indicate that the government should promote personality development of youth by upgrading the monetary value given to high achievers in school after completion of their programs or degree in order to spur more interest in appreciating education and shaping the behaviour of individuals. Also, the government should provide sports-academic scholarship schemes that would serve as criteria and support for talented youth in order to assist them in pursuing their dreams and ensuring that all citizens are catered for educationally. In addition, the government should also place more emphasis on music as a way of bringing about a better life for youth. As well, the government should ensure the effective regulation of television programs so that they are educative, meaningful and relevant to the progress of youth in order to empower and reduce the problem of unemployment and poverty among youth.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".