Introduction. Ageing time beings: Temporality and ethics in old ages
Bibliographic record
Abstract
What can we learn about temporality by studying different ways of measuring time, institutional time regimes, and (a)typical experiences and creations of time when growing older? This introduction sets perspectives on this question from the anthropologies of ageing, ethics, and temporality. Understanding humans as time beings, we argue that attention to connections between large‐scale history, collective temporal registers, and small‐scale singularities of the experience of time can reveal and destabilize common representations of ageing and time. We propose an analytical direction that acknowledges and attends to situations of uncertainty and suffering, while also foregrounding questions about ‘the good’, not only through paying attention to cultural values such as ‘active ageing’, ‘filial piety’, or ‘desired dependency’ (and critiques of them), but also smaller scale, oppositional, and atypical values and poetics of ageing and time. We introduce the contributions in the special issue with close‐up ethnographies from Canada, Denmark, India, Japan, Kyrgyzstan, Uganda, and the United States, and the core argument across the contributions regarding how time manifests in multiple ways but is ontologically groundless. This lays the ground for critiquing various dogmas about age and time and opens up possibilities of affording plural temporalities in social life.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".