MUTUALITY AMONG EAST ASIAN DIASPORA AT A NATURALLY OCCURRING RETIREMENT COMMUNITY IN METRO VANCOUVER
Bibliographic record
Abstract
Abstract How did East Asians who were retiring abroad cope with the pandemic? This paper traces the development of a Naturally-Occurring Retirement Community (NORC) in Vancouver through the efforts of various community organizations. As borders closed during the pandemic, a group of community-dwelling East Asian older adults found themselves remaining in Metro Vancouver through the winter. They formed a group chat and began regular video chats. They shared virtual events, supermarket deals, and neighbourhood walks. A younger member stepped up to ask after members of the informal group whom were absent or unwell. Topics of discussions ranged from the wellbeing of members and their spouses, securing medical appointments, translating forms to be filled, friends back home, leisure activities, and the lives of their children or grandchildren. The group was formally served by facilitators of a seniors’ club at a non-profit. At the same time, they existed as an informal network marked by mutual care. We discuss the health-promoting nature of mutuality in this NORC as compared to other neighbourhoods to theorize the conditions needed for the emergence of Gemeinschaft NORC in urban areas. This paper sketches a theory of mutuality as the key which transforms dyadic peer support into community cohesion. We explore the implications of mutuality on formal programs in order to harness their potential to have population-level spillover effects for the wellbeing of community members just beyond their direct reach. We further speculate that mutuality is that which was lost in modernity, and is the reason culture is medicine.
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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 | 0.001 | 0.001 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".