On older person/place transformations: Towards a more‐than‐representational geography of aging in rural Canada
Bibliographic record
Abstract
The spatial variability of population aging in rural areas of Canada, and the demographic processes that underlie these areal patterns, are reasonably well understood. Research to date emphasizes processes of population redistribution (e.g., net out‐migration), regional economic change (e.g., resource‐based economic restructuring), and chronologically‐centred models of bodily decline as the major features of population aging in rural contexts. This literature has informed a wide range of gerontological research and policy, but there is much more to be said about becoming older in rural Canada. In this paper, we present the outline of a post‐representational approach to rural aging. We consider the influence of relational and non‐representational forces acting on the experience of aging in rural Canada. We then draw on reflections of earlier work in a particular geographic setting as a means to tease out “more‐than‐representational” considerations for discussion. We also echo recent calls to address a “blind spot” in geographic scholarship that overlooks the considerable extent to which older persons re‐shape their community environments. We conclude with an invitation for a greater engagement with older person/place transformations, including closer attention to the processes and performances of “aging‐through‐place” in other Canadian settings.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.020 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".