What Is Retirement? A Review and Assessment of Alternative Concepts and Measures
Bibliographic record
Abstract
Because the concept of retirement is prominent in both popular thinking and academic studies, it would be helpful if the notion were analytically sound, could be measured with precision, and would make possible comparisons of patterns of retirement over time and among different populations. This paper reviews and assesses the many concepts and measures that have been proposed, summarizing them in groupings that reflect non-participation or reduced participation in the labour force, receipt of pension income, end-of-career employment, self-assessed retirement, or combinations of those characteristics. It concludes that there is no agreed measure and that no one measure dominates. Instead, new proposed measures continue to take account of additional refinements as new data sets become available, thereby further restricting possible comparisons. The confusing array of definitions reflects the practical problem that underlies the concept of retirement: It is an essentially negative notion, a notion of what people are not doing - namely, that they are not working. A more positive approach would be to focus, instead, on what people are doing, including especially their involvement in non-market activities that are socially productive, even if those activities do not contribute to national income as conventionally measured.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".