Bibliographic record
Abstract
This study uses annual data between 1990 and 2010, and employs Dynamic Ordinary Least Square (DOLS) method to examine what determines poverty level in Nigeria. Unlike many studies, we measure poverty with poverty index generated from combination of per worker agricultural value added, real per capita income and consumption per capita using principal component analysis and common measurement of poverty (i.e. per capita real income). We first remove the trend component of our dependent variables (poverty index), using Butterworth filter and then regressed them on the important variables of interest. The findings show negative relationship between political right in levels and poverty, but positive relationship was found when political right was differenced. This result was not statistically significant. Political terror was found to reduce poverty with statistically significant result in levels when per capita real income was used for poverty, and became positively related with poverty when differenced. The result was statistically significant. We found that civil liberty was positively related to poverty, but the result was not statistically significant. Democracy was noted for reducing poverty with statistically significant result, while the increase in population and poverty were positively related with statistically significant result.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".