Understanding Bridge Employment Through the Lenses of Kaleidoscope Career Model
Bibliographic record
Abstract
Demographic transition has changed the landscape of retirement worldwide. For many, retirement is no longer an endpoint to working life, but rather a beginning to another stretch of workforce participation in the form of bridge employment (Engelhardt, 2012). The academic literature has examined why people return to work after retirement. However, there is a dearth of literature examining how to make workplaces suitable for people coming out of retirement. Research demonstrates that retirement is a critical life event, and thus, has significant impact on peoples’ attitude towards subsequent phases of their lives (Wang & Shi, 2014). Based on this premise, we explored how retirees re-construct retirement from hindsight. Another purpose of our research was to examine whether retirees distinguish between pre-and post-retirement work. Our analysis of 26 in-depth interviews involving bridge employees revealed that though retirement meant different things to different participants, the view that retirement was a point of departure from an obligatory bread-winner role was a robust one. This seemingly liberating conceptualization of retirement changed the priorities of people in their post-retirement work. Using the Kaleidoscope Career Model, we analyzed how bridge employees distinguish between their pre and post-retirement work. We found that putting oneself first, making a meaningful contribution and having flexibility to pursue pleasure took priority over career goals. Getting a realistic picture of how retirees prioritize different goals in their lives and how they situate work in post- retirement life is an important contribution to the career literature, one which we feel can spawn future research.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".