Aging Filipina migrants’ experiences of transnational end-of-life care and loss over time
Bibliographic record
Abstract
This article addresses experiences of transnational end-of-life care among aging Filipina migrants before and during COVID-19. de Leon addresses the emotional costs associated with loving and losing kin from a distance both before and during the pandemic, drawing on their autobiographical account of distant care by proxy during their aunt’s wake and funeral. Blower-Nassiri highlights the exacerbated fears and anxieties around dying, illness, and end-of-life among aging migrants, drawing on two life histories of retired nurses who recalled moments of loss and being absent for end-of-life events, such as funerals, before and during the pandemic. Together, de Leon and Blower-Nassiri provide an intimate portrait of three Filipina migrants’ experiences with end-of-life care and loss. They further address the limitations of the life course framework in their consideration of how care carries on, across generations through end-of-life practices and rituals that signal an accrual of loss over time.
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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 | 0.002 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".