THINKING GLOBALLY, ACTING LOCALLY FOR AGE-FRIENDLINESS: THE AGE-FRIENDLY DC INITIATIVE
Bibliographic record
Abstract
Ageing and urbanization are among the most transformative demographic dynamics of the 21st century. In order to ensure that urban environments are responsive to the needs of residents across their life course, the World Health Organization (WHO) promotes the creation of Age-Friendly Cities and Communities. During 2012–2015, WHO conducted research to develop a set of core indicators of age-friendliness which measure the physical and social environment, quality of life, and equity. As part of this project, a study was conducted to measure these indicators in 15 communities worldwide. Washington, DC was one of the test sites. Some of the key findings from the indicator assessment, which was led by Age-Friendly DC staff with the cooperation of the Mayor-appointed Task Force and several government agencies, were that when inequities could be analysed there were profound differences across race and income. Importantly, this focus on equity revealed the need for more disaggregated data by age and geography at the local level. Subsequently, the WHO core indicators informed the development of the Age-Friendly DC Livability Survey, which was conducted in 2016 to track progress in implementing the Age-Friendly DC Strategic Plan. The survey found that progress was being made in home internet access while improvement is still necessary in the wheelchair accessibility of homes. The Age-Friendly DC initiative is exemplary of how the outcomes of the global project to develop metrics for age-friendliness were translated into local strategic plans and actions to create an age-friendly urban environment in the nation’s capital.
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 | 0.022 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".