“That is when you realize your age”—A spatial approach to age(ing)
Bibliographic record
Abstract
Age is a conceptual challenge for geographical research due to its twofold character as a marker of difference (age) and a dynamic process (ageing). The fluidity of the ageing process makes it difficult to employ age as an analytic variable for empirical research, perhaps even more so than for other social categories such as gender, ethnicity, social status, or sexual orientation. Drawing on qualitative research with 18 expert interviews and 4 focus group discussions with older people (n = 26) from diverse backgrounds in Berlin (Germany), this paper argues for a spatial perspective to grasp the individual, continuous process of ageing. Based on the spatial settings of (1) places of recreation, (2) places of work, and (3) home as examples, our empirical findings reveal how older people become aware of their own ageing through specific places and how the process of ageing is perceived in relation to both people of other age groups and one's personal lived lifetime. The intersectional approach of our research thus demonstrates how social diversity shapes the experience of later life. The paper concludes by proposing three ways how a spatial perspective on the ageing process can advance debates within geographies of ageing.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".