Організація кіберспортивних змагань для військових та ветеранів війни з інвалідністю або обмеженнями повсякденного функціонування
Bibliographic record
Abstract
The organization of esports competitions for military personnel and veterans with disabilities or limitations in daily functioning has significant potential to improve their mental and physical condition, as well as their social integration. The relevance of the study is emphasized by the need to develop new effective rehabilitation methods that can be accessible and attractive to veterans. Objective. To analyze the experience of using esports competitions and events for military personnel and war veterans with disabilities or limitations of daily functioning in foreign countries and to develop an algorithm for their organization and conduct. Methods. Analysis of special scientific literature, comparison, surveys, systematization, and methods of statistical analysis. Results. Esports is increasingly seen as an effective means of rehabilitation for military personnel and war veterans with disabilities. Studies and initiatives in the United States, the United Kingdom, Canada, Australia, and Ukraine show that participation in esports competitions contributes to the physical, cognitive, and social rehabilitation of veterans. Organizations such as the Warrior GMR Foundation, Stack Up, Battle Brothers Gaming, Veterans Gaming Australia, and Rehabilitation through Gaming actively engage veterans in esports events, giving them the opportunity to feel community support and develop new skills. A survey of military personnel and war veterans showed that 49 % of respondents had full access to digital devices under combat conditions, which indicates the active use of modern technologies on the battlefield. However, only 41.8 % of the military personnel regularly or sometimes play computer games to relax and recover from stressful situations, while 58.2 % do not have this opportunity. The majority of respondents (42.4 %) consider computer games to be partially effective in reducing stress. Based on a detailed analysis of foreign and domestic practical experience, an algorithm for organizing and conducting esports competitions for war veterans with disabilities was developed, which will contribute to their rehabilitation, social integration, and skill development. Keywords: esports, competitions, military personnel, war veterans, algorithm, disability, integration.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".