Bibliographic record
Abstract
In 2007, the World Health Organization (WHO) launched their Age-Friendly Cities (AFCs) program in response to the global trend towards aging populations and increasing urbanization (WHO, 2007). The WHO anticipates that by 2050, approximately 22% of the global population will be over the age of 60 with the majority residing in sub/urban areas (WHO, 2007). In the Canadian context, one in four residents will be 65 years or older by 2036 with well over 70% residing in (sub)urban communities (PHAC, 2011). Moreover, the intensity of older adults will be most acutely felt in small and mid-sized cities where the mean older adult dependency ratio will grow by 103% and 90% respectfully by 2036 (Hartt and Biglieri, 2018). Given the certainty of demographic change and the heterogeneity of the older adult population (Garvin et al, 2012; Stafford and Baldwin, 2018), there is a time sensitive need to understand how to support older adults who desire to age in place – that is, to live safely and independently in their existing communities (WHO, 2007). Since the beginning of the twenty-first century, there has been a renaissance of academic literature linking built environments and human health in order to address the rising rates of chronic diseases, a warming climate, loss of green space to urban sprawl, and automobile dependency (Frank et al, 2003, 2006). Geographers and planners have played an important role in acknowledging the importance of local built environments to population health and wellbeing throughout the life course (Laws, 1993; Frank et al, 2006; Andrews et al, 2007; Gilroy, 2008; Cutchin, 2009; Garvin et al, 2012; Kerr et al, 2012).
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".