Creating a sense of home: assisted living in the Timiskaming District
Bibliographic record
Abstract
With a rapidly growing aging population, there is an \nimminent need for assisted living. This is especially true for \nNorthern Ontario; specifically, more remote northern communities \nsuch as ones within the Timiskaming District. These communities \nsuffer from being under serviced, leaving aging members of these \nareas unable to receive the levels of care they require. \nMoreover, when assisted living is offered, it is within the form \nof facility-based design. This creates a problem as these facilities \ndo not take into account the northern identity, but also, they are \nnot proven to be beneficial to the well-being and independence of \nsenior adults. \nThis thesis presents an alternative way that assisted living \ncan be designed for the well-being of aging individuals through \nvillage – based, community design, that takes into account the \nnorthern identity. This can be done through creating a strong \ncommunity amongst the residents, but also within the surrounding \nexternal community of Kirkland Lake, Ontario.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".