Bibliographic record
Abstract
Co-organised by the Centre for Research and Expertise in Social Gerontology (CREGES) and the research team dedicated to the study of aging and social exclusion (VIES), this interdisciplinary symposium studies diverse groups of older adults (immigrants, lesbians, users of health and social services, and older women) and how they experience various forms of social inclusion and exclusion in their daily lives, be it by public authorities and the community in general. The contributions also analyse how social exclusion and inclusion affect older adults. Dr. Marier provides an analysis of the concept of autonomy, how it is used in the health and social service sector, and how its use in evaluation tools can result in diverse forms of social exclusion with marginalized groups experiencing less access to services. Ms. Beauchamp presents results of a series of interviews with lesbian older adults on their experience with agism and heterosexism. Her findings reveal a lack of social recognition and visibility, but also a willingness on their part to play a more predominant societal role. Dr. Brotman studies the lived experiences of immigrant older adults and uncover the common structural stressors that affect their well-being. She will highlight the roles of the community and of government to support older immigrants and to ensure their social inclusion. Dr. Wallach discusses the effects of Western society’s beauty norms on older women and how an aging female body can lead older women to experience social exclusion, not only by others but largely by self-social exclusion.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.011 | 0.042 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.006 |
| 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".