Bridge Employment Experience: an Exploratory Approach
Bibliographic record
Abstract
In spite of a growing tendency among present day retirees to engage in bridge jobs before their final exit from the labour force, academic attention directed to understanding experiences of bridge employees is insufficient. Our paper intends to fill in this gap in literature via an exploratory approach. We conduct 26 semi-structured interviews among retirees currently engaged in bridge employment to understand why they entered bridge employment and what were their expectations from and experiences in bridge employment. We unearthed several interesting categories and concepts under each theme. Love of job, social connection and financial need emerged as prime motivators behind post-retirement work. Regarding expectation, bridge employees expected flexibility and psychological enjoyment from work and were cognizant of the fact that they may have to take a cut in pay to accommodate for their expectations. However, there were also participants who took on bridge jobs as a new “career” phase and thus were more vocal about extrinsic rewards (pay, promotion etc.). Lastly, though bridge employees were overall satisfied with their work, benefits and social interactions; some of them faced social disapproval because of the perception that they are taking away jobs from people who need it more. To our knowledge, our paper is one of the pioneers in exploring bridge employment experience from the perspective of the retirees. The findings of our research shed light on hitherto unexplored areas in bridge employment research, which will help HR managers and policy makers in designing mutually beneficial jobs and positions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".