LGBTQIA+ Elderly: How male gay men are affected by etarism
Bibliographic record
Abstract
The elderly population (60 years or older) is the fastest growing in Brazil. By 2060 these individuals will represent a quarter of the Brazilian population, at least 25.5%, according to IBGE (Brazilian Institute of Geography and Statistics) data from 2018. For this reason, both political, social, psychological, and health issues, as well as Geriatrics and Gerontology intensify their assessment and methodology, to adapt to all these problems that grow exponentially over time (Camarano and Kanso, 2010). The present work seeks to develop research related to how the different prejudices (homophobia and etatism) directed to the people of this population affected/affect their future, their present, and their past with a focus on the LGBTQIA+ population, focusing on the cut of gay men of this community, to understand how these people lived and how they were of Homophobia in times when they needed to hide their sexuality, how it affected or affects the way they perceived themselves back then and how they perceive themselves today, being elderly people and being within the LGBTQIA+ community, through semi-structured online entertainment. The adversities in the living conditions that this population faces can cause serious psychological problems, adding this to the lack of knowledge and research in this area, explains that aging within the the LGBTQIA+ community is an aspect of this population that needs to be discussed and studied, thus contributing to a better quality of life and aging of these people, without stigmas or prejudices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".