THE IMPACTS OF LESBIAN, GAY, BISEXUAL AND TRANS (LGBT) LIFE COURSES AND IDENTITIES IN LATER LIFE
Bibliographic record
Abstract
Previous research has shown that older lesbian, gay, bisexual and transgender (LGBT) people have very diverse life courses. Some have lived outwardly conventional lives while others have lived more unconventional ones. It has also been shown that older LGBT people use (and do not use) sexual and gender identity labels very differently in different historical, cultural and geographical locations and at different stages of their lives. The papers in this symposium explore some of the impacts of these diverse life courses and identities in later life, in order to contribute to more nuanced and less homogenised ways of understanding LGBT older people’s experiences and needs. Fredriksen-Goldsen’s paper is a systematic review and narrative analysis LGBT ageing studies spanning 25 years, showing the tension between individualized and interconnected identities for LGBT ageing. Kong’s paper explores some of the ways in which the identities and lives of older gay men living in Hong Kong were affected by participation in a research project, demonstrating how this become a site for the construction of new collective identities. King’s paper examines how the housing experiences, concerns and preferences of older LGBT people are intersected and complicated by questions of identity with significant implications for policy and practice. Meanwhile, Jones’ paper compares the experiences of older people with bisexual relationship histories who do and do not identify as bisexual, in order to add to our understanding of the contextually specific way in which identities are used, and the effects this may have in later life.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".