Bibliographic record
Abstract
Researchers and policy makers from across the globe are increasingly concerned about the accelerating numbers of older people in their societies despite public health concerns about Covid-19.In the Xi Administration in 2021 in China, there are concerns about the inadequacy of pension funds, of growing pressures on health systems, and on the inability of shrinking numbers of younger people to carry the burden of older people.This article focuses on such health issues in China, where the origin of Covid-19 has been found and where older people have become a rapidly expanding proportion of the population.While resources do need to be targeted on the vulnerable older people, the presumption that older people as a whole are an economic and health burden must be questioned.This is arguably an ageist approach that needs to be combated by locating how bio-medical views on aging seep into health policy spaces in China that position negative perceptions of aging as both individual and populational problems.The article then moves to observe the implications of bio-medicine for older people in China in terms of "vulnerable" aging but deconstruct such "fixed" explanations by juxtaposing active aging as key narrative that epitomizes "declining to decline" as espoused by health sciences in a paradoxical public pandemic in an era defined by COVID-19.
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 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 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".