Age-Friendly City Construction and Its Practical Application: A Case Study on the Application of Service Demand Research for the Elderly in Guangzhou, China
Bibliographic record
Abstract
This study is based on the age-friendly community framework advocated by the World Health Organization as the research premise. Through the continuous international academic research cooperation between China and Canada, reference is made to the age-friendly community strategy of Alberta, Canada and the construction practice of the age-friendly city in Calgary, carry out a special investigation on the needs of elderly care services in Guangzhou, apply the international framework of an age-friendly city to the construction of an age-friendly city in Guangzhou, and the construction of specific cities and communities in Guangdong–Hong Kong– Macao Greater Bay Area in China. Based on the demographic development and policy background of China and Guangzhou, this study implements the needs of the national strategy of actively cope with population aging. In preparation for building Guangzhou into an age-friendly city and a city with a livable environment integrated with its own characteristics, providing a theoretical framework, and aimed for building a model city of healthy aging and livable living in the Guangdong–Hong Kong–Macao Greater Bay Area. It could be a sample of healthy aging cities in the Bay Area and models that can be used for reference by other cities.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".