Transgender Ukrainians and the Need for LGBTQIA+ Inclusion in National and International Refugee Policies
Bibliographic record
Abstract
Over 7 million Ukrainians have become refugees since Russia's invasion of Ukraine. As a result, countries around the world have opened their doors and invited Ukrainians to seek refuge from the ongoing conflict. However, not all Ukrainians have experienced this conflict equally, with queer and transgender Ukrainians facing increased risks of gender-based violence, discrimination, and difficulties crossing borders due to inaccurate gender markers on their identification. This policy brief focuses on the unique challenges LGBTQIA+ Ukrainians have faced while fleeing the conflict. I provide an analysis of the persistent exclusion of LGBTQIA+ people within sexual and gender-based violence policies and refugee policies (like the Women, Peace and Security agenda and Feminist Foreign Policy initiatives) due to marginalization and systemic discrimination. This paper argues that the Government of Canada can address this issue, not just for Ukrainian refugees but for all refugees, by implementing LGBTQIA+ inclusive policies to address the gaps in Canada's current system. It should be noted, this policy brief examines the conflict from February 2022 to April 2022. Given that this is an ongoing conflict, some statistics may have changed, however, the lack of support for LGBTQIA+ refugees in national and international refugee policy remains a pressing issue.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".