Bibliographic record
Abstract
The experience of growing older in Britain is ethnically and culturally differentiated. Yet, mainstream gerontological and sociological approaches to ageing have failed to examine, in any detail, the interconnections between agency, ageing and ethnic diversity. This means that theories of ageing often exclude experiences outside the 'traditional' domain. Here, traditional includes those theories of ageing that have relied on western (British/American) concepts to measure personal power and fulfillment in later life. Yet, the meanings attached to agency, empowerment, autonomy and independence vary in relation to the specificities of time, space and culture. Despite this, western culturally specific concepts tend to underpin the notion of 'successful ageing'. This paper questions both the effect this has on how agency and empowerment are theorised and the extent to which some experiences are excluded by definition. It presents findings from an ESRC qualitative research project on womenís experiences of agency and dis/empowerment in later life across ethnic diversity. There were differences, for instance, in the timing of old age, what constituted control and agency and womenís relationship with their bodies, as they grew older. The paper considers the extent to which current gerontological and sociological theories and concepts of ageing adequately represent ethnic and cultural differences in what it means to grow older.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.086 | 0.016 |
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".