Bibliographic record
Abstract
What does it mean to grow older? This paper explored this question through discourse analysis. Life course and interpretive perspectives provided a theoretical framework for the study. Semi-structured interviews with a purposive sample of 23 participants provided data on their aging experience which were analyzed as a discourse. Initial analysis identified four narrative frames: accumulated age, experience and wisdom; changed social status; onset of health problems; and increased likelihood of death. Further analysis introduced two contrasting discursive subjects of “powerful seniors” and “declining elders-ambiguous ancestors”. The results show that aging leads to a powerful social status of elderhood and seniority, on one hand, while overtime, it results in decline and ambiguity in one's social relations. The findings are discussed in light of global and local discourses on aging and elderhood. Whereas on one hand social anthropologists construct elders as agents of influence who constitute a bridge between the past and the future, on the other hand, medical science and public health construct older adults as sick, weak, and dependent subjects whose place is in respite and long-term care facilities. Clearly, discourses on aging are particular ways of speaking to and about older adults and serve as surveillance on everyday practice—individual behavior and collective responses toward the elderly. It is recommended that, to the best of their abilities, older adults be engaged in peace building and dispute resolution efforts, child protection and child rearing decision-making, community problem solving, and the teaching of history from experience. It is also recommended that older adults be given income and housing assistance as well as increased access to affordable healthcare services to attenuate decline and loss.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".