UNDERSTANDING INCLUSIVE PHYSICAL PLACES AND VIRTUAL SPACES: THE FORTITUDE OF OLDER MARGINALIZED COMMUNITIES
Bibliographic record
Abstract
Abstract The World Health Organization (WHO) Age-Friendly Cities and Communities (AFCC) agenda emphasizes collaboration among older individuals, local groups, councils, and businesses to enhance communities. This involves improvements in several social and environmental domains including transportation, outdoor spaces, volunteering, employment opportunities, and leisure and community services. IncludeAge, a UK Economic and Social Research Council funded project, addresses multifaceted aspects of age-friendly communities beyond the emphasis on social and physical environments, highlighting the importance of belonging and identity alongside cultural and life course experiences. Focusing on physical and online places and spaces, IncludeAge takes intersectional and life course perspectives to understanding the inclusion of mid-older (40+) people who are often marginalized: those with intellectual disabilities and LGBT+ individuals. Employing a transdisciplinary, innovative mixed-methods approach and guided by Community-Based Participatory Research principles, our longitudinal study uses life course interviews, GIS story mapping and social network analysis to document experiences of inclusion and exclusion in past lives and current real-time. Preliminary insights underscore the importance of the cultural and historical dynamics of experienced stigma and discrimination, the value of ‘hidden spaces and places’ and ‘experiences on the periphery’. We use the Intersectional Place Perspective (IPP) theoretical model to aid interpretation. Through co-developed story maps and individual narratives, we aim to amplify the voices of marginalized groups in designing potential solutions to exclusionary experiences and practices, fostering empowerment and social inclusion. Our research will advance understanding of AFCC and community inclusion as a human right, thereby supporting meaningful everyday lives for our focal populations.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| 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".