Bibliographic record
Abstract
By now, most community planners and local government decision makers are acutely aware of the changing demographic character of North American communities and of the challenges that this change represents for community services and physical infrastructure. In brief, the retirement of the baby boomer generation has already begun, and in just two decades it is projected that 20% of the North American population will be 65 years or older (Menec et al, 2015; Statistics Canada, 2019). In response, the Age-Friendly Communities (AFC) movement has acquired considerable policy and research traction since its launch by the World Health Organization (WHO) in 2007 (World Health Organization, 2007). Beginning in 2013, the Province of Ontario has made several incremental efforts to expand its AFC policy efforts based on three linked strategic policies and investments – that is, the launch of the Finding the Right Fit Age Friendly Communities Planning Guide , the Ontario AFC Planning Grants Program, and funding for an AFC Outreach and Community Support Program (Ontario Seniors’ Secretariat, 2013). Despite growing attention to the needs of older adults throughout Ontario for nearly a decade, consideration of the needs of Indigenous (ie First Nations, Métis, and Inuit) peoples has been conspicuously absent in recent provincial and municipal AFC research, community engagement, and policy activities (Health Council of Canada, 2013; Ramage-Morin and Bougie, 2017). Where there has been consideration and inclusion of Indigenous voices in local AFC initiatives, those voices are largely represented by First Nations peoples and organizations. This is often based on the assumption that Indigenous perspectives are broadly similar and can be ‘captured’ by First Nations representation, or that other Indigenous peoples and communities such as the Métis do not exist throughout much of Ontario.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".