Intimate networks of care: Perceptions of intergenerational family care and experiences of ageing among Chinese midlife and older lesbians and gay men
Bibliographic record
Abstract
This article examines how lesbians and gay men imagine and build their 'intimate networks of care' and negotiate moral expectations towards intergenerational family care as they age. To date, little is known about the strength and complexities of different intimate ties or the role of intergenerational dynamics in shaping ageing sexual minority people's care needs and choices. Based on narrative interviews with ageing Chinese lesbians and gay men, the findings reveal their experiences of constantly juggling their ties with families of origin, moral values around intergenerational care and the urge to receive support from and offer support to chosen networks of people. Participants exercised agency in expanding their networks of care by building friendship and (online and offline) community networks for mutual care and support in later life. Nevertheless, as evidenced by the centrality of ageing with(out) children, and the moral obligation of caring for parents in participants' narratives, participants experienced tensions between enacting what was considered morally right/wrong and developing networks of care that were perceived as emotionally intimate. Linking relational sociology with the sociology of morality, we discuss the conceptual utility of 'intimate networks of care' for sociological theorising of the linkages between sexuality, care and relational lives.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| 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".