Ageing and Community: Introduction to the Special Issue
Bibliographic record
Abstract
ABSTRACT Population ageing is one of the major contemporary issues facing societies across the world. Originally framed as a major social and economic challenge, demographic ageing is now beginning to be seen as offering huge potential to individuals as well as to their communities. It is this positive potential that we explore in this issue by utilising two key disciplinary approaches—social gerontology and social/community psychology. In this introduction, we argue that focus on only one or the other of these perspectives is limiting. Instead, a more critical approach is needed that incorporates the strengths of both disciplines in order to build a more complete and stronger understanding of ageing and community. Thus, a focus on social gerontology highlights ageing issues and explores the diversity of older people and their interactions with community. By incorporating a social/community psychology approach, there is potential to complement this body of work through a deeper level of analysis around community, as well as individual and relational dimensions. The result is a special issue that brings together these two perspectives to address some of the shortcomings of approaching ageing through solely one disciplinary lens. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".