I May Be Frail But I Ain’t No Failure
Bibliographic record
Abstract
The terms "successful aging" and "the frail elderly" are now commonly used in aging research, but biomedical researchers may be unaware of the possible unintended negative consequences of their use. A commonly used operational definition of successful aging (high cognitive and physical function, low probability of disease, and active engagement with life) reflects values not necessarily shared by other cultures or even by older persons in our own culture. Other definitions for "a good old age" have been proposed. The adjective "successful" implies that those who do not meet its definition are unsuccessful or a failure. Labels such as "frail" predispose the person described to the phenomenon of identity spread, whereby the label becomes the master identity. Labels encourage us to regard someone as "other". Yet only 10-15% of us will die without a significant period of disability. Research has demonstrated that older persons internalize stereotypes of aging, which can have important short- and long-term effects. The language and theories of social scientists can be poorly understood by those outside of their field, yet biomedical clinicians and researchers should be aware of this literature so that unnecessary suffering is not unintentionally inflicted on our patients and our future selves.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".