Building a Link between Retirement Planning in the Civil Service and Entrepreneurship Development in Nigeria
Bibliographic record
Abstract
Retiree involvement in entrepreneurship is known to address their wellbeing challenges hence the interest to ascertain the determinants and level of their contribution among retirees of the Civil Service of Akwa Ibom State of Nigeria. Data were obtained through the use of structured Questionnaire which was administered during the annual verifi cation exercise of retirees. Multi regression analysis using four functional forms of linear, double log, semi log and exponential; Pearson Product Moment Correlation, chi square and T-test methods were used to test the hypotheses. The basis for the selection of the bestfit model and lead equation was the one with relatively highest R value, lowest number of significant, lower error of estimation and appropriateness of a prior signs. Double log provided the best option. The result showed that all the independent variables were signifi cant at 0.05 level of probability. These relative effects suggest that these factors if given adequate corresponding attention would lead to a healthy and increased post retirement involvement in entrepreneurship. It is recommended that measures be taken to invigorate pre-retirement training, hitherto ignored as an essential and integral part of the retirement planning process of the Civil Service of the State. Key words: Retirement planning; Civil service; Entrepreneurship development; 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 | 0.005 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".