Bibliographic record
Abstract
The Canadian population is aging. Seniors are becoming the fastest growing age group as a result of the aging of the baby boom generation, and a lowered fertility rate and an increase in life expectancy in the millennial generation. Currently, the population of Canada is approximately 35 million people, of which five million are aged 65 and over. It is estimated that by 2051, about one in four Canadians will be aged 65 and over. At the rate our population is aging, it is foreseeable that the cost of services for the elderly will escalate rapidly as a result of an increased demand for services but a lack of caregivers and facilities to support them. As such, there is a growing demand for new models of living and care for seniors with a shift towards a more economically sustainable, community-oriented schema, where the collaboration and mutual support between the residents could ease the economic and social burden for society. The author has developed a new approach with regards to designing for an aging population – a conceptual kit of parts known as the Model For Age-Inclusive Care. The thesis proposes the development of an age-inclusive multi-service and care hub to reintegrate the elderly into the social fabric of the city by using underperforming, under-utilized commercial developments as an activator. In essence, this thesis will attempt to connect between the more disparate parts of society through the incorporation of places with potential for development in an attempt to present a model of symbiotic community space aging.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".