Eradication of Poverty and Hunger in Nigeria: Issues and Options for Attainment of Millennium Development Goals
Bibliographic record
Abstract
The saying that, “a country that cannot feed its poor, cannot assure sleep to its rich” is very apt. Eradicating poverty and hunger is the greatest challenge facing the world today. Progress has been made over the last generation in improving living standards in paper therefore, examined the challenges and options open in the poorest countries, but over a billion people still struggle in absolute poverty. This paper therefore, examined the challenges and options open in order to eradicate poverty in Nigeria. It was discovered that bad governance, poor economic policies, ill-timed reforms, corruption, lack of commitment to poverty eradication programmes and poor investment in rural agriculture were the prime cause(s) of hunger and poverty. All hope is not lost because there are options government can pursue such as ensuring broad-based growth good governance, tackling unemployment, basic needs approach etc in order to eradicate poverty and hunger. The paper is of the view that to eradicate hunger and poverty, economic growth is necessary but not a sufficient condition. There is need to induce broad-based growth and provide social services and infrastructure aimed at reducing/eradicating the depth and sovereignty of poverty and hunger across the country. Key words: Poverty; Eradication; Hunger; challenges; agriculture
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".