Effective Assessment Framework: Sustainability of Post Amnesty Programme in Niger Delta Region for National Development
Bibliographic record
Abstract
The Niger Delta is said to be the world.s largest wetland. The oil and gas in that region represents 97% of Nigeria.s foreign exchange earnings and this accounts for a major chunk of the wealth of the country. The Niger Delta has for some years been the site of major confrontations between the people and the Nigerian government.s security forces, resulting in extrajudicial executions, arbitrary detentions, and draconian restrictions on the rights to freedom of expression, association, and assembly. To proffer solution, Federal government came up with post-amnesty program. Based on this, the study sought; (i) To determine the level of effectiveness of the implementation of the Post Amnesty Programme since its inception in 2009, (ii) determine how effective evaluation method can sustain Post Amnesty programme beyond Amnesty peace agreement in Niger Delta. The research design was descriptive survey method. The population of the study was 4,798,519 million youths [15-39years][last Census2006] in Delta state, Bayelsa state and River state. The study made use of qualitative and quantitative data analyses. The sample size was 400 using Taro Yamane formula. Purposive sampling technique was used to select the respondents in each of the states, while hypothesis one was tested using Friedman Chi-Square test statistics, hypothesis two was tested using One Sample Kolmogorov-Smirnov (Z) test. Effective evaluation method significantly maintains the Post Amnesty Programme in Nigeria (X2(friedman)cal = 137.098 > X2Zcritical = 1.96, p < 0.05). The study recommended that the Amnesty International and all other agencies involved in peace building and in managing conflict in Niger Delta should borrow a leaf from other countries. management techniques such as in Canada and USA.
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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 | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".