From Canada to Kircubbin: learning from North America on housing an ageing population – Part 1
Bibliographic record
Abstract
Purpose The purpose of this study was to learn lessons from North America on housing an ageing population, both in terms of supporting people to “age in place”, and available options for those who need/wish to move. Design/methodology/approach The project, funded by the Winston Churchill Memorial Trust, comprised a six‐week travel fellowship to the USA and Canada to meet with housing professionals from the public and private sectors and find out about best practice initiatives and efficient models for housing older people. Findings This report is written in two parts. This, the first, considers models which are successfully facilitating individuals and communities to support each other to age in place, for example, the Beacon Hill Village model which has taken off in the USA in a big way. Technology can, and will, also play an important role in enhancing the lives of older people in the future, but housing is really about people and it will be people who will make the real difference on this issue. Originality/value This was a unique opportunity to learn lessons from North America on how to effectively meet the needs of the older population, now and in the future.
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 | 0.004 | 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 | 0.036 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".