Reexamining the boundaries of the ‘normal’ in ageing
Bibliographic record
Abstract
Textbooks and policy documents tend to present the boundary between normal and abnormal ageing as natural and clearly demarcated. In this study, we trouble the notion of natural and clearly demarcated boundaries between normal and abnormal ageing by considering how these boundaries have been established and maintained in present-day Western contexts. We draw on both Canguilhem's discussion of the normal and the abnormal and Foucault's emphasis on the role of the sociohistorical context in the social practice of boundary generation. In doing so, we critically examine common conceptualizations of normal and abnormal ageing, including those found in antiageing science, successful ageing and healthy ageing policy discourses and in health education textbooks. We argue that the growing emphasis on 'healthy' ageing both reflects and shapes the societal views of those individuals who are not able to remain disease-free and represents a kind of mystification of ageing where ageing without functional or cognitive decline is instituted as the norm. Awareness of the role that the social context plays in shaping definitions of normal and abnormal ageing encourages critical consideration of the effects that Western conceptualizations of normal ageing may have for older adults who continue to age with cognitive or functional decline.
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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.081 |
| Scholarly communication | 0.008 | 0.021 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".