Bibliographic record
Abstract
The world's urban populations are ageing rapidly and there is a pressing need to examine the needs and experiences of older adults in the city. There is additionally a crucial need to understand the pointedly gendered dynamics of the ageing process in the city. This research begins a critical investigation into this topic in Toronto from a feminist perspective, using a strengths-based perspective to examine the experiences of 9 older women living in the west end of Toronto and navigating the urban environment. It also uses insights from 2 service providers who work with older women in Toronto. The paper then engages these findings with an analysis of two policy documents designed to address ageing in the city, the World Health Organization's "Global Age-Friendly Cities: A Guide" (2007) and the City of Toronto's "Toronto Seniors Strategy: Towards an Age Friendly City" (2011), and discusses some revealing discrepancies. It finds significant strengths and considerations that the research subjects harness and navigate in the urban environment, as well as important intersections between age, gender, race, income and immigration status. It discusses the care responsibilities of many of the women and how they affect their interactions with the urban environment. The paper also point to directions for future research to better understand the ageing process from a critical, intersectional perspective and locate it in a current discussion of urban theory.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".