Towards age-inclusive sustainable development goals: exploring the potential role and contributions of community development
Bibliographic record
Abstract
Worldwide, societies are experiencing unprecedented shifts in their age compositions. For the first time in human history, the number of older people will surpass the number of children that are under the age of fourteen representing one of the ‘biggest social transformations’ societies will experience. The great shift in demographics demand that sustainable development efforts are age-inclusive and support the well-being of people throughout their life course – including the later life years. The purpose of this article is 2-fold. First, we delineate the linkages between the proposed sustainable development goals (SDGs) and development issues related to older persons and an ageing population, arguing that the success of the SDGs also rests on the ability to address such issues. Second, we explore community development's role in the implementation of the SDGs and addressing age-related development issues, proposing that community development's unique perspectives, values and approaches contribute to innovative development pathways conducive to age-inclusive sustainable development.
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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".