THE DYNAMICS OF MANAGING THE RISING UNEMPLOYMENT HURDLES IN NIGERIA: A FOCUS ON IMO STATE
Bibliographic record
Abstract
This study examines the causes and effects of unemployment in Nigeria with particular reference to Imo State. The National Bureau of Statistics (NBS) report on unemployment showed that the country unemployment rate increased to 27.1% in the second quarter of the year (Q2 2020) compared to 23.1% of the third quarter (Q3 2018). Imo State both in terms of unemployment and underemployment records 48.7% and 75.s% respectively. The report is worrisome as it poses threat to the State development, security and peaceful co-existence. The main objective is to look into the causes, effects and solution to unemployment problem. The study reviewed extant literature anchored on the theory of Neo-liberalism of Marxist. Also, historical and descriptive research design with secondary method as sources of its data collection were utilized, while content analysis was adopted. The result of the study reveals that unemployment is a multidimensional problem caused by holistic variables vis: corruption, ineptitude leadership, neglect of agriculture, weak infrastructure, with negative implication as poverty, low income and insecurity. The study recommends comprehensive approaches which include functional governance, provision of enabling environment and re-calibration of policies that will strengthen effective governance, investment and economic
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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 | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".