MAPPING COMMUNITIES OF CONCERNS FOR OLDER ADULTS: A CASE STUDY OF CALGARY ON URBAN GROWTH AND RESOURCE ALLOCATION
Bibliographic record
Abstract
Objectives: This study examines whether a city has grown in a way of providing an equal access to social and community resources for older adults by 1) evaluating the city’s land use and transportation policies as well as social policies at the neighborhood level, and 2) identifying the communities of concerns and the limited social and community resources for older adults. Methods: With spatial data regarding community services from The City of Calgary and the 2016 census data from Statistics Canada, this study analyzes the distribution of social and community resources within the city of Calgary using the ArcGIS software as well as maps out communities lacking the resources for aging in place. Results: When comparing the communities in the urban core, older adults living in the suburbs have a relatively lower level of accessibility to senior-specific resources as well as overall social and community resources. A lack of transportation options, other than driving, and a separate land use also decrease the accessibility to those resources for older adults. Discussion: Findings suggest that communities, particularly located in the edge of the city, are often experiencing a lack of social and community services due to the delayed service provision resulting from deficiencies in the capital and operating costs for the supply. Findings also have implications that a city has to ensure a better accessibility to the services for older adults who may experience mobility challenges as they age.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".