Global Food Crisis: Public Capital Expenditure and Agricultural Output in Nigeria
Bibliographic record
Abstract
The focus of this paper is to examine the effects of global food crisis on developing countries which most times are considered most vulnerable due to a number of factors ranging from conflicts in the region, to poverty and inequality, and factors that affect policies on agriculture. Issues concerning the use of food as alternative energy sources are still in its infant stages in Africa, and may not be relevant. The paper investigates the impact of capital expenditure on agriculture and credit to agricultural sector on the output of agriculture in Nigeria. Annual data covering 1990 – 2004 were used. Unit roots of the series were examined using Augmented Dickey – Fuller techniques. The overall results indicate that output of agriculture is positively related to capital expenditure on the sector but negatively related to the credit to agriculture. The paper therefore recommends improvements in government capital expenditure on agriculture. Credit policies and institutions should target beneficiaries (rural farmers).
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".