The new meaning of retirement for bridge employees: Situating bridge employment through the lens of the Kaleidoscope Career Model
Bibliographic record
Abstract
Abstract Retirees re‐entering the workforce, popularly termed as bridge employment, is a phenomenon that is anticipated to increase in the coming years. Though research establishes that these employees have unique aspirations and work motives (see Mazumdar et al., 2020), primary research on how the retirement transition and bridge employment shape each other is scarce. This is troubling because a better understanding of the aspirations and motives of potential employees is an important step in designing suitable employee development strategies. To fill this gap in the literature, our paper explores the significance of retirement for those retirees who engage in bridge employment. We also explore whether bridge employment is unique from pre‐retirement employment. We interviewed 26 bridge employees and analyzed their narrations using the thematic analysis method. We utilized the Kaleidoscope Career Model by Mainiero and Sullivan (2005) to contextualize our analysis. Our study reveals that bridge employees uniquely reconstruct the meaning of retirement as a frontier between “prioritizing the obligations” and “prioritizing self.” Our findings also demonstrate how this view allows retirees to prioritize self‐directed goals during bridge employment. Our paper enriches the human resource development literature on careers and retirement by examining it from the vantage point of bridge employees. We shed light on how re‐framing the narratives of retirement helps distinguish between bridge employment and pre‐retirement employment for retirees. Better understanding this distinction can help lay the foundation for crafting suitable employee development programs for improved motivation and retention.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".