Determinants of Food Inflation in Ondo State, Nigeria
Bibliographic record
Abstract
Abstracts This study investigated the determinants of food inflation in Ondo state, Nigeria from 2017 to 2023. The research made use of monthly secondary data from Ondo state budget office from the first quarter of 2017 to the fourth quarter in 2023. The study deployed descriptive statistics, correlation matrix, and Ordinary Least Square (OLS) for the study. The result revealed that factors responsible of food inflation in Ondo state, Nigeria revealed that the root cause for the rising food inflation in Ondo state, Nigeria is caused mainly by insecurity, then followed by price of Premium Motor Spirit (petrol), Internal Generated Revenue (IGR) and rainfall (climate change). In conclusion, the results from this study implies that both state and national securities personnel’sneed to form a synergy (joint security team)in order to tackle insecurity headlong,as this will ensure optimum local food production output which in turn will clash the skyrocketing food prices within the state. Furthermore, the state government should invest massively into mechanize farming (agriculture), hydroponics farming, as well as improved seedling that can withstand the adverse effect of climate change. Lastly, the state government should have support genuine farmers will improve seedlings and fertilizers at a subsidized rate. This work can serve as a comprehensive guide in tackling the crises of food inflation in Ondo state. Nigeria.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".