Contributors’ Involvement in Pension Fund Investments Decision Making and Retirees Standard of Living in University of Lagos, Nigeria
Bibliographic record
Abstract
Management of the pension scheme in Nigeria had been inundated with several and diverse challenges ranging from corruption and mismanagement of funds for some decades. As a result retirees were not able to access their pension benefits leading to some dying without access to their fund. The government then introduced the contributory pension system in a pension Act of 2004 which was amended in 2014, as a measure to minimize the sufferings of retirees as well as allay the fears of workers. However, the problems of pension are yet to abate and retirees are still groaning under unstable welfare. This study examined contributors’ involvement in pension funds investment decision making and retirees’ standard of living. The study adopted convergent parallel research design with population being non-academic staff of University of Lagos. The population of the study was 5098 and sample size was set at 100 respondents using Taro Yamane’s (1967) formula. Response rate of the validated questionnaire was 91%. Descriptive and inferential statistics (linear regression) were employed in analysis of data. The study found that contributors’ decision making on pension fund investment exerted a positive significant effect on retirees standard of living (β=.46, R2=0.49, t=10.57, p
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".