Bridge Employment: Transitions from Career Employment to Retirement and Beyond
Bibliographic record
Abstract
The last quarter of the twentieth century and the early years of the twenty-first ushered in far-reaching, rapid change in terms of socio-demographic factors, economic circumstances, and working conditions on a scale never before witnessed in history. In particular, the sustained rise in life expectancy over recent decades and the steep fall in the birth rate have accelerated the process of population ageing, generating powerful, worldwide effects (Lutz et al. 2008). While there are still significant differences between the more developed, less developed, and least developed countries (United Nations 2013), the gap between them is rapidly closing (Bongaarts 2004), and in the context of these global demographic shifts, employee retirement has become an important, indeed a core, element of political and socio-economic discourse and a key factor in the area of human resource management (HRM) (Wang and Shi 2014). The goal is to maintain older workers at work, extend working life, and avoid mass exodus from the labour 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 teacher head, 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".