Researching LGBTQ+ homelessness and building social justice in the UK & the US: methods, ethics, recruitment
Bibliographic record
Abstract
LGBTQ+ homelessness research is an emerging area growing in importance in the UK, the US, Canada and Europe. Research to date indicates that methodology and participant recruitment are particularly challenging for this group. Sexual orientation and gender identity, as well as homelessness and poverty are taboo topics that are often stigmatized. Homelessness for LGBTQ+ people is therefore under-reported both by third sector organizations and governments. The scale of the problem is difficult to determine, resulting in the de-prioritization of support, funding and policy change. Drawing on research outcomes from projects in England, Scotland and the US, this paper explores possibilities for conducting research into LGBTQ+ homelessness can happen, and why such research is vital to world-building and epistemic justice. We consider the delicate question of whether we can accurately and ethically produce data on LGBTQ+ homelessness, what the repercussions are for those currently experiencing homelessness, and whether it is still important to pursue such research given the potential harms.
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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 | 0.045 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".