Bibliographic record
Abstract
Living beyond ninety years is not always seen so positively, and a number of our cases described in the last two chapters have illustrated how fragile life can be as one enters the tenth decade. At least twenty-five of our sample of forty cases reached age ninety years. Many of them were by then in poor health, ten of them dying within two years. Nevertheless, there were others who continued to flourish beyond ninety years and even beyond ninety-five years. The latter could be said of at least eleven of our cases, more than one-quarter of the whole sample. In this chapter, we describe the lives of the six persons, three women and three men, in the sample whom we were able to interview up to and in some cases beyond their middle nineties. Did awareness of their exceptional longevity influence the way that they perceived meaning in their lives? The oldest women One of the most remarkable stories in our sample of forty cases was that of Elsie Darby , whom we first mentioned in Chapter 4. In Chapter 7, we described her life in a residential care home where she came to live at age eighty-six after a difficult period in which she was in a stressful relationship with her third husband, whom she subsequently divorced, and also suffered a heart attack. Yet Elsie succeeded in leaving residential care five years later at age ninety-one and returning to sheltered housing, where she lived a further six years. How did she manage it? She herself later said that she had been spurred to leave the home by a visitor who had said to her that she was ‘taking someone's place’! Elsie had the good fortune that all her furniture had been kept in store at no cost by friends of the family. Therefore, it was relatively easy for her to set up home again.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.014 |
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".