Intention to unretire
Bibliographic record
Abstract
Purpose The purpose of this study is to identify antecedents of intentions to unretire among a group of retirees that included both those who had not returned to the workforce since their retirement and those who had previously unretired. Design/methodology/approach A cross‐sectional survey collected data from 460 recent retirees between the ages of 50 and 70. Findings Results of hierarchical regression indicated that retirees are more likely to remain retired if they feel financially secure and have a positive retirement experience. Conversely, they are more likely to intend to return to the workforce if they experience financial worries, wish to upgrade their skills or miss aspects of their former jobs. Practical implications Aging boomers who anticipate early retirement have created a dwindling labor pool. Simultaneously, the global pension crisis has impacted on the financial decisions of retirees. A trend to abolish mandatory retirement and/or increase mandatory age in various countries provides individuals with more freedom in their retirement decisions. Accordingly, managers must be creative in their HR planning strategies to retain or recruit skilled retirees. Originality/value Previous research has addressed retirement as a final stage, however, given simultaneous global demographic changes and economic concerns, this study provides new knowledge regarding the factors that push and pull retirees to participate in the labor market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".