CONTEXTS AND CULTURES: CHANGING NEIGHBORHOODS, SOCIAL NETWORKS, AND RETIREMENT
Bibliographic record
Abstract
During adulthood, people face a variety of changes, both individual and contextual, which can be associated with concomitant well-being; for example, changes in the composition of their neighborhood, social networks, and work status. In adapting to change or transition, adults often seek to retain the status quo or preserve continuity, in terms of their individual psychological attributes and their surroundings (Atchley, 1989). The four presentations in this symposium use both qualitative and quantitative data to examine changing contexts for a diverse range of samples - Canadian, African American, and Arab American – and how adaptations to change are related to well-being. Based on the Convoy Model (Kahn & Antonucci 1980), Chauhan examines the impact of a telephone helpline on the social networks of older Canadians. Newton examines Canadians’ experiences of personal identity and associated well-being within the context of retirement. Issues of trust and psychological well-being are the focus of Ajrouch and colleagues’ examination of neighborhood change in metro-Detroit. Finally, Versey looks at older African Americans’ experience of aging in place within a recently-gentrified neighborhood in New York. Discussion of the four talks will be facilitated by Dr. Toni Antonucci. These presentations highlight the wealth of individual, social, and community contexts within which midlife and older adults strive to maintain well-being.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".