Assessment of the Willingness of Ebonyi State Government to Adopt the Contributory Pension Scheme in Nigeria
Bibliographic record
Abstract
In the year 2004 Nigeria keyed into the contributory pension scheme. This was precipitated by the daunting challenges pensioners face in accessing their pensions under the non-contributory pension scheme which was introduced in 1979 by Act 102. The major challenges that confronted the non-contributory pension scheme were embezzlement of pension funds, and delay in the payment of retirement benefits to retirees and on time. These negatively affected the welfare of retirees. This study reviewed reasons why Ebonyi State government was stuck with the non-contributory pension scheme despite these challenges. The study was conducted in Ebonyi State and the study population was made up of 108 staffs of the Department of Pension’s, office of the Head of Service and staff of the Sub-Treasury, Ebonyi State Ministry of Finance. Questionnaire was administered to 108 respondents, out of which 85 were returned. Data generated reveal government’s unwillingness to submit for passage the contributory pension bill to the Ebonyi State House of Assembly (51.8%) and excessive bureaucracy in the adoption of the contributory pension scheme (60%), that contributory pension scheme is difficult to manage (56.5%) and so forth as reasons why government has refused to introduce the contributory pension scheme. The study recommends the adoption of the contributory pension scheme as alternative to the challenges pensioners face in the management of the non-contributory pension scheme in Ebonyi State Nigeria.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".