Bibliographic record
Abstract
In this vignette, we examine the challenges and opportunities of aging in urban Canada. In addition to sharing our own experiences of growing older in a major Canadian city, we also call upon the work we have been doing to help the city become a better place for everyone to age. As local government efforts have continued to fall short, residents (like us) have begun to take matters into their own hands. In this vignette, we summarize some of the challenges for creating an age-friendly community in Calgary, introduce our organizations, and outline some of the obstacles and opportunities we have faced. Finally, we provide some recommendations for other organizations looking to make an impact in their communities. One of the most concerning aspects of aging in Calgary is that the majority of housing available for seniors is extremely expensive. It is far cheaper for people to stay in their own homes, only paying for taxes, utilities, and maintenance. Another problem in Calgary is the practice of keeping roads and cycle paths safe during the winter, but not the sidewalks. Ploughing and piling snow in front of bus stops makes boarding the bus difficult and puts pedestrians at risk by forcing them into designated bicycle lanes. Public transportation is not subsidized by the government, and bus pass prices have recently been raised. This means that low-income seniors may not be able to afford the bus, which could contribute to increased isolation and, as a result, a decline in wellbeing. Older people in our local community often feel like they aren’t a priority.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.131 | 0.017 |
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".