Global economic meltdown and its effects on human capital development in Nigeria: Lessons and way forward
Bibliographic record
Abstract
Global economies around the world have experienced the most traumatic moments in the last one-decade. The crisis has been described by scholars, as perhaps been the worst financial crisis since the great economic depression of the 1930s. This paper lucidly examines the effects of global economic recession on the development of human capital with reference to Nigeria nation. The objectives of the paper among others are (i) To establish the level of the impact of global economic recession on development of skills of human capital in Nigeria (ii) To examine if there is any significant relationship between global economic recession and the motivation of human capital development in Nigeria among others. The paper uses survey method with two research hypotheses. Questionnaires were administered among academic staff of two Nigerian universities in the southwest part of Nigeria. Findings showed that the global economic recession has great impact on the development of skills of human capital in Nigeria. Findings also revealed that there exists a positive relationship between global economic recession and training and development of human capital in Nigeria. The paper offers useful policy recommendations, which include the need for government and appropriate agencies to put in place policies such as enabling environment that will lead to the growth and development of human capital in Nigeria. Government needs to put forward policies that minimize cost at all levels, maximize efficiency of output, training and retraining of goods hands; and that there is need to encourage better motivation of workers at every sector of the economy amongst others.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".