Regulatory Framework for Artificial Intelligence in the Legal System of Pakistan
Bibliographic record
Abstract
Progressively more people are living to at least the age of 60 years and at least a quarter of the global population is expected to be 60 years or older by 2050 (United Nations, 2023). This demographic transition, driven by declining fertility rates and increased life expectancy, is accompanied by a notable trend: more people are returning to the workplace or taking up part-time jobs after retirement, which implies that the number of retirees doing so is on the rise. In the United States, about 29 percent of retirees resume work (DE Silver, 2023), and 47 percent of men aged from 60 to 64 years go back to work within the first ten years of retirement in Canada (Statistic Canada, 2023). Such changes in the social contract require that current and future advancements in post-retirement employment be analyzed and understood about antecedents and consequences of career concepts and management of human capital in organizations. There is relatively little literature published on retirement, however, the few extant literature are silent on what motivates retirees back into work and the effects on organizational performance. Therefore, this paper attempts to fill this gap by reviewing the literature on post-retirement employment, with particular emphasis on antecedents and consequences of decisions to re-employment retirees. It stresses the significance of integration and synthesis of findings for better understanding of the subject by specialists of different branches of knowledge, including sociology, psychology, and economics in the framework of HRM for the sake of improved strategic planning and policy-making. Finally, an analysis is made regarding demographic effects on workers in the organization as well as the effects of the retiring baby-boomers and the shortage of workers expected to ensue. It speaks about the possibilities of reemploying older workers as having implications to reduce workforce shortages, especially when it comes to specialized occupational positions. Last but not least; the paper sums up the social equity functions of retirees, retirees' responsibilities in family and community, and difficulties experienced in the process of retirement. Thus, the goal to expand the existing knowledge about retirees’ quality of life and the effects of work after retirement on the individual and organizational levels will be achieved through attending to the aspects identified above. Indeed, this research is valuable for enriching modern theories on career development and human resource management, and for understanding how retirees’ skills and experience can be utilized in the interest of both the employment market and the social well-being of the community.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".