Bibliographic record
Abstract
This vignette is based on my lived experience over time. My first job as a social worker in the early 1960s was with older adults (in one of the first home care programs in Canada). I learned much from them on how to live my life and how to optimize life as I grew older. My practice has focused on clinical work and community development, especially in the areas of health and gerontology. With students and colleagues, we developed an empowerment model of practice in long-term care with resident councils, initiatives with families, and staff training. In the 1990s, a colleague enticed me to become a member of the Toronto Council on Aging, in order to raise awareness of the needs of older adults, improve their quality of life, foster their involvement in all aspects of community life, and support the experience of aging through education and leadership. I speak from my own experience, combined with what I have learned from older friends and from the wider community of older people through informal contacts and also research. I have lived in Saskatchewan, Manitoba, and now Ontario; in small towns, mid-sized cities, and for over 50 years in Toronto. While Toronto has great diversity and a rich array of social, recreation, education, volunteer, and employment possibilities, it is so very large and complex that it is difficult to know what these opportunities are and how to access them. Similarly, health and social services can be difficult to navigate, even for someone like myself who has experience and skills in this area.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".