LGBT-Military in Ukraine during Russia’s War against Ukraine: Tendencies in Social Attitudes and Perspectives for Implementation of Anti-Discrimination Policies in the Army of Ukraine
Bibliographic record
Abstract
The relevance of visibility of LGBT people in different spheres of social life in Ukraine, particularly in the Armed Forces and voluntary military formations, increased after the beginning of Russia’s full-scale invasion of Ukraine on 24 February 2022, in spite of the fact that the social movement of LGBT military personnel and veterans began to be established in 2018 during the ATO-OUF. The question of visibility shed light on such aspects as social attitudes towards LGBT people involved in the country’s defense, implementation of anti-discrimination policies as well as problems and needs of LGBT people in the army that used to be silenced and suppressed for a long time in the institution of the army.The article focuses on the analysis of the phenomenon of establishment of the LGBT military movement in the Ukrainian army in 2018 as well as identifies some issues that LGBT people face while serving in the military: homophobia, risks of bullying, and the inability to legalize relations with partners.Additionally, the article examines examples of anti-discrimination policies that were implemented in Sweden, the USA, the United Kingdom, and Canada as possibilities for changes in Ukraine after their adaptation to the local context.Lastly, the article presents findings of the survey conducted for the research titled “First Complex Research of LGBT+ Military in Ukraine” by the NGO “Ukrainian LGBT+ Military and Veterans for Equal Rights”, which identified key tendencies in the attitudes of Ukrainians towards the participation of LGBT people in the defense of Ukraine during the war as well as possibilities for the implementation of antidiscrimination policies in the army. After the analysis, it was found that in spite of regional differences, the majority of Ukrainians (71.8%) accept that LGBT people participate in the defense of Ukraine on an equal basis with heterosexual people. Additionally, 63% of respondents stated that norms, regulations and rules of the Armed Forces of Ukraine have to defend the rights of LGBT military personnel. Therefore, it can be concluded that Ukrainians have a predominantly positive attitude towards LGBT military personnel and support the protection of their rights in the army.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".