Aging queer in a pandemic: intersectionalities and perceptions
Bibliographic record
Abstract
Purpose The purpose of this study is to highlight the experiences and issues of an overlooked demographic: older LGBTQ + adults in the US, in the context of the COVID-19 pandemic. This allows the authors to explore possible changes in policy and practice regarding the management of the pandemic with attention to elderly LGBTQ. Design/methodology/approach Building on the authors’ experience in disaster research and a study of older LGBTQ + adults in the San Francisco Bay Area, the authors analyze key trends in COVID-19 pandemic management while drawing lessons from the AIDS epidemic. Findings The authors have found that LGBTQ + people, especially older and transgender individuals, have unique experiences with hazards and public safety and healthcare professionals and organizations (e.g. heteronormative care, traumatic insensitivity, deprioritizing essential treatments as elective). Second, older LGBTQ + adults' perceptions of state responses to pandemics were heavily influenced by experiences with the HIV/AIDS pandemic. And third, experiences with the COVID-19 pandemic have important implications for preventing, responding to and recovering from future epidemics/pandemics. Originality/value The authors point to two parallel implications of this work. The first entails novel approaches to queering disaster prevention, response and recovery. And the second is to connect the management of the COVID-19 pandemic to the principles of harm reduction developed by grassroots organizations to suggest new ways to think about contagion and organize physical distancing, while still socializing to take care of people’s physical and mental health, especially the more marginalized like elderly LGBTQ + people.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".