HISTORICAL PREREQUISITES FOR THE DEVELOPMENT OF ATHLETICS IN KHERSON REGION
Bibliographic record
Abstract
The article analyzes the historical preconditions for the development of athletics in the Kherson Region. The state of physical culture and sports activities during the twentieth century is revealed. It is determined that athletics was one of the most popular sports in the former USSR. Before the Second World War, athletics clubs emerged in all sports clubs. Moreover, already in 1938 the improvised team of the best athletes of Kherson took part in the republican championship in Kharkiv. It was revealed that in the personal championship of Kherson Nadiya Shapar fulfilled the normative requirements of the title "Master of Sports of the USSR" in cross-country. She became the first master of sports in Kherson. Before the start of the war, there were 11345 athletes in the Kherson region, who were united in 102 sports organizations. It was established that in the postwar period, the organization of physical culture work in educational institutions was built on the principle of a sports club, and only in October 1957, the first three grassroots societies of the student GOST "Petrel" were established in Kherson. Constant sports work was carried out in 26 sports. This company was a leader in athletics in the region for a long time. Under the leadership of GOST "Petrel" sports games, athletics, various relay races were held among students and working youth of Kherson region. Students of Kherson universities were constantly members of the athletics teams of the region and the republic. Valery Borzov became the most famous athlete among the representatives of Kherson region. He entered the history of world sports as the first Soviet European champion in athletics. The victory at the Olympics in Munich in the 100- meter race was brilliant. After winning the Olympics from 1975 to 1977, three times in a row Valery won the title of European champion in the short distance of 60 meters indoors. At the 1976 Olympic Games in Montreal, the athlete won medals again, but this time bronze. The success of one athlete could not change the state of affairs in the sports economy of the region, and in the late 80’s it became obvious. Only more than 30 years later, another outstanding athlete was able to represent the Kherson Region on the same high level at the Olympic Games – Yuriy Bilonog.
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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 | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".